Deepflare

deepflare

I was receiving a particular forwarded meme of a famous Hollywood actor over WhatsApp from so many of my contacts since last a few days. This one might have gone viral. It superimposes the actor’s face on the body of Superhero Hulk and makes him do some nasty stuffs. Oh! Quite ridiculous but people are liking it. The video was made with extreme perfection and the finishing touch was superb. I came to know later on that it was made by an internet user only. Was it deepfake? I came to know about deepfake a few years back while I was doing some research to write an article on AI. Deepfake is a technique to replace an image or video of a person with another. We keep on seeing numerous memes, photoshopped images nowadays. Deepfake is of a different level. Deepfake generates visual content with the help of machine learning and artificial intelligence. Deepfake is basically Deeplearning. What the hell! So many jargons here. Deeplearning uses an artificial neural network basically. The network is made to learn and improvise learning. The network is often supervised, semi-supervised or unsupervised. Ultimately it aims for perfection in learning and generating a predictive output. For Deepfake, the output is almost ‘perfect fake’. Deepfake started as an AI experiment and caught public attention through swapped celebrity faces in porn in early 2017. A reddit user named ‘Deepfake’ bombarded internet in 2017 with fake celebrity porn videos. He made those using opensource machine learning tools. Monalisa deepfake - A Samsung lab from Russia showed in 2019 that it is possible to fake video from a single photo The superimpose thing was not new. But the perfection and simplicity of the process was striking. Since then, Deepfaking gained a lot of popularity. In July’17, University of Washington researchers developed a deepfaking algorithm and turned audio clips into a realistic, lip-synced video of the then US president Obama. It became viral instantly. Yes, deepfaking started journey from the academician’s laboratory but became accessible to the end user after that. Vitalik Buterin sings Pink Floyd’s “Money” in a deepfake video made by Alethea AI (If the video doesn't play, click here to watch) Deepfakes have become extremely photorealistic. AI can generate any kind of facial expression. was an app, launched by an anonymous developer. It was able to unclothe a clothed woman and create porn video from that. The controversial app was taken down after launch in 2019. Not only a celebrity but also a common guy can become victim of deepfaking. How does it sound? DeepNude CZ Binance as Jet Lee in a deepfake video by Althea AI Technology is both boon and bane. Deepfaking can be disastrous for an individual. It can play pivotal role in international diplomacy also. Just imagine the impact of a deepfaking video which depicts Chinese President Xi Jinping declaring that COVID19 was manufactured in the Chinese laboratory. Before the actual reality comes out, the video will be forwarded million times in social media and will have ripple effects. The technology is advancing at a rapid rate and it is often difficult to understand the difference between the original and a Deepfake. What’s the way out? , the viral deepfake maker, is building AI tools to identify deepfakes. They want to record deepflakes in the blockchain and make it immutable. AI will be used to dig data from blockchain and provide accurate results about deepfaking query. It is interesting. Alethea AI A traditional database can be tampered any time. We need AI to fight with AI! Only a blockchain can provide the necessary data set for training the AI algorithm. is one company which is fighting deepfakes with its advanced media forensics platform. Their platform runs on Ethereum blockchain. They do the video fingerprinting at source. That means, they cryptographically hash a video at source before logging on the blockchain to detect whether the video was tampered or not. Amber Video Technology is supposed to evolve. It is unjustified and impossible to ban a technology. Once upon a time, Cuban president Fidel Castro banned mobile phone in Cuba due to the suspicion that the technology would be used by US for surveillance. They had to lift the ban ultimately in 2008. We can’t go backward. We need to embrace deepfaking. Deepfaking will create a new entertainment field. It is already being used in some digital games. Imagine creating your perfect avatar with accurate expressions and taking part in a multi-player combat game with VR. It can be so real! Synthetic contents will enter into your life to entertain you in different manners very soon. But yes, we need to keep a good check to stop misuse. Deepfakes are here to stay. Accept that! Cover Image Source

Will Deepfakes Be Part of Our Lives?

Image: Regula's survey shows that, while average industry losses from deepfake fraud sit at $440,000, 37% of Crypto companies are losing over $500,000. RESTON, Va — A new “Deepfake Trends 2024” study by Regula, a global developer of forensic devices and identity verification solutions, highlights the pressing financial challenges posed by deepfakes to the Crypto industry. The study reveals that while the industry averages a loss of $440,000 due to advanced fraud techniques, a significant portion – 37% of Crypto companies – are facing losses exceeding half a million dollars each. Deepfakes, which manipulate audio and video to create convincing yet fraudulent content, have become a major concern for more than half of the organizations in the Crypto sector. According to the study, 53% of businesses in the industry have encountered incidents of video deepfakes. Notably, the Crypto firms are particularly vulnerable to audio deepfake fraud, with 57% of respondents reporting such incidents, compared to 50% in other sectors. This heightened susceptibility may stem from the industry’s reliance on specific verification methods. The study identifies multifactor authentication (MFA) as the most widely employed mitigation strategy, utilized by 57% of respondents. In contrast, other industries often prefer biometric verification as their top choice. Audio verification is one of the components within MFA, enabling users to confirm their identity through various methods, such as a password, text message code, and their voice. The impact of deepfake fraud on the crypto industry extends beyond immediate financial losses. Organizations face substantial costs, with 35% of respondents citing legal expenses as a significant burden. Penalties and fines were reported by 33% of organizations, reflecting financial repercussions from regulatory violations or legal settlements. Additionally, 27% of respondents acknowledge reputational risk as a key concern, highlighting the potential damage to trust and credibility that deepfake incidents can cause. “Losing $500,000 to a single type of fraud is staggering for any organization, especially in a fast-paced industry like Crypto,” said Henry Patishman, Executive Vice President of Identity Verification Solutions at Regula. “This finding underscores the need for robust security measures, grounded in best practice. As the threat landscape evolves, it’s crucial for companies to adopt a liveness-centric approach, focusing on real-time verification of people and only physical objects.” Find more insights on deepfake fraud in the survey report. Read the full version on our website. Additional resources: ●  	Identity Fraud Statistics 2023: How Businesses Respond to the Issue ●  	Data from 2022: One-Third of Global Businesses Already Hit by Voice and Video Deepfake Fraud ●  	Financial Organizations Challenged by the Growing Community of Digital Nomads ●  	The Anatomy of ID Document Liveness Detection The research was initiated by Regula and conducted by Sapio Research in August 2024 using an online survey of 575 business decision-makers across the Financial Services (including Traditional Banking and FinTech), Crypto, Technology, Telecommunications, Aviation, Healthcare, and Law Enforcement sectors. The respondent geography included Germany, Mexico, the UAE, the US, and Singapore. About Regula Regula is a global developer of forensic devices and identity verification solutions. With our 30+ years of experience in forensic research and the largest library of document templates in the world, we create breakthrough technologies in document and biometric verification. Our hardware and software solutions allow over 1,000 organizations and 80 border control authorities globally to provide top-notch client service without compromising safety, security or speed. Regula was repeatedly named a Representative Vendor in the Gartner® Market Guide for Identity Verification. Learn more at www.regulaforensics.com. Image : Regula's survey shows that, while average industry losses from deepfake fraud sit at $440,000, 37% of Crypto companies are losing over $500,000. Image Image Regula's survey shows that, while average industry losses from deepfake fraud sit at $440,000, 37% of Crypto companies are losing over $500,000. RESTON, Va — A new “ Deepfake Trends 2024 ” study by Regula , a global developer of forensic devices and identity verification solutions, highlights the pressing financial challenges posed by deepfakes to the Crypto industry. The study reveals that while the industry averages a loss of $440,000 due to advanced fraud techniques, a significant portion – 37% of Crypto companies – are facing losses exceeding half a million dollars each. RESTON, Va — — Deepfake Trends 2024 Deepfake Trends 2024 Regula Regula Deepfakes, which manipulate audio and video to create convincing yet fraudulent content, have become a major concern for more than half of the organizations in the Crypto sector. According to the study, 53% of businesses in the industry have encountered incidents of video deepfakes. Notably, the Crypto firms are particularly vulnerable to audio deepfake fraud, with 57% of respondents reporting such incidents, compared to 50% in other sectors. This heightened susceptibility may stem from the industry’s reliance on specific verification methods. The study identifies multifactor authentication (MFA) as the most widely employed mitigation strategy, utilized by 57% of respondents. In contrast, other industries often prefer biometric verification as their top choice. Audio verification is one of the components within MFA, enabling users to confirm their identity through various methods, such as a password, text message code, and their voice. The impact of deepfake fraud on the crypto industry extends beyond immediate financial losses. Organizations face substantial costs, with 35% of respondents citing legal expenses as a significant burden. Penalties and fines were reported by 33% of organizations, reflecting financial repercussions from regulatory violations or legal settlements. Additionally, 27% of respondents acknowledge reputational risk as a key concern, highlighting the potential damage to trust and credibility that deepfake incidents can cause. “Losing $500,000 to a single type of fraud is staggering for any organization, especially in a fast-paced industry like Crypto,” said Henry Patishman, Executive Vice President of Identity Verification Solutions at Regula. “This finding underscores the need for robust security measures, grounded in best practice. As the threat landscape evolves, it’s crucial for companies to adopt a liveness-centric approach, focusing on real-time verification of people and only physical objects.” “Losing $500,000 to a single type of fraud is staggering for any organization, especially in a fast-paced industry like Crypto,” said Henry Patishman, Executive Vice President of Identity Verification Solutions at Regula. “ This finding underscores the need for robust security measures, grounded in best practice. As the threat landscape evolves, it’s crucial for companies to adopt a liveness-centric approach, focusing on real-time verification of people and only physical objects.” “Losing $500,000 to a single type of fraud is staggering for any organization, especially in a fast-paced industry like Crypto,” This finding underscores the need for robust security measures, grounded in best practice. As the threat landscape evolves, it’s crucial for companies to adopt a liveness-centric approach, focusing on real-time verification of people and only physical objects.” Find more insights on deepfake fraud in the survey report. Read the full version on our website. Find more insights Additional resources: Additional resources: ● Identity Fraud Statistics 2023: How Businesses Respond to the Issue Identity Fraud Statistics 2023: How Businesses Respond to the Issue Identity Fraud Statistics 2023: How Businesses Respond to the Issue ●  	Data from 2022: One-Third of Global Businesses Already Hit by Voice and Video Deepfake Fraud One-Third of Global Businesses Already Hit by Voice and Video Deepfake Fraud One-Third of Global Businesses Already Hit by Voice and Video Deepfake Fraud ● Financial Organizations Challenged by the Growing Community of Digital Nomads Financial Organizations Challenged by the Growing Community of Digital Nomads Financial Organizations Challenged by the Growing Community of Digital Nomads ● The Anatomy of ID Document Liveness Detection The Anatomy of ID Document Liveness Detection The Anatomy of ID Document Liveness Detection The research was initiated by Regula and conducted by Sapio Research in August 2024 using an online survey of 575 business decision-makers across the Financial Services (including Traditional Banking and FinTech), Crypto, Technology, Telecommunications, Aviation, Healthcare, and Law Enforcement sectors. The respondent geography included Germany, Mexico, the UAE, the US, and Singapore. The research was initiated by Regula and conducted by Sapio Research in August 2024 using an online survey of 575 business decision-makers across the Financial Services (including Traditional Banking and FinTech), Crypto, Technology, Telecommunications, Aviation, Healthcare, and Law Enforcement sectors. The respondent geography included Germany, Mexico, the UAE, the US, and Singapore. The research was initiated by Regula and conducted by Sapio Research in August 2024 using an online survey of 575 business decision-makers across the Financial Services (including Traditional Banking and FinTech), Crypto, Technology, Telecommunications, Aviation, Healthcare, and Law Enforcement sectors. The respondent geography included Germany, Mexico, the UAE, the US, and Singapore. About Regula Regula is a global developer of forensic devices and identity verification solutions. With our 30+ years of experience in forensic research and the largest library of document templates in the world, we create breakthrough technologies in document and biometric verification. Our hardware and software solutions allow over 1,000 organizations and 80 border control authorities globally to provide top-notch client service without compromising safety, security or speed. Regula was repeatedly named a Representative Vendor in the Gartner® Market Guide for Identity Verification. Learn more at www.regulaforensics.com . www.regulaforensics.com www.regulaforensics.com

Over One-Third of Crypto Companies Lose More Than $500,000 Each to Deepfake Fraud

ከአንድ ሶስተኛ በላይ የሚሆኑ የCrypto ኩባንያዎች በጥልቅ ማጭበርበር እያንዳንዳቸው ከ500,000 ዶላር በላይ ያጣሉ

এক-তৃতীয়াংশেরও বেশি ক্রিপ্টো কোম্পানি ডিপফেক জালিয়াতির জন্য $500,000-এর বেশি হারায়

Más de un tercio de las empresas de criptomonedas pierden más de 500.000 dólares cada una por fraudes de deepfake

Yli kolmasosa salausalan yrityksistä menettää kukin yli 500 000 dollaria syväväärennöksistä

Máis dun terzo das empresas criptográficas perden máis de 500.000 dólares cada unha por unha fraude falsa

A kriptográfiai cégek több mint egyharmada egyenként több mint 500 000 dollárt veszít mélyhamisítás miatt

יותר משליש מחברות הקריפטו מפסידות יותר מ-500,000 דולר כל אחת כתוצאה מהונאות Deepfake

仮想通貨企業の3分の1以上がディープフェイク詐欺で50万ドル以上を失っている

Крипто-компаниялардын үчтөн биринен көбү Deepfake алдамчылыгынан ар бири 500 000 доллардан ашык акча жоготушту

Daugiau nei trečdalis kriptovaliutų kompanijų praranda daugiau nei 500 000 USD dėl netikro sukčiavimo

Mahigit Isang-Ikatlo ng Mga Kumpanya ng Crypto ang Nawalan ng Higit sa $500,000 Bawat Isa sa Deepfake Fraud

超过三分之一的加密货币公司因 Deepfake 欺诈损失超过 50 万美元

In the dynamic political environment of the United States, the rapidly approaching 2024 presidential election is becoming the arena for a new kind of battle—one against the insidious rise of AI-generated deepfakes. These sophisticated synthetic constructs, capable of distorting reality, have started a legislative marathon across the nation, as state leaders press forward with decisive measures to uphold the integrity of voter information. There is no federal law that deals with deepfakes, leaving it up to individual states to legislate. The top official at the Federal Election Commission (FEC) has signaled expectations for the issue to reach a resolution later this year. This forecast hints at a significant stretch of the 2024 election period potentially unfolding with this brand of misinformation remaining unchecked. Recently, bridging the partisan divide with a shared vision for election integrity, Sens. Amy Klobuchar of Minnesota and Lisa Murkowski of Alaska joined forces. Their collaborative legislative endeavor presents a forward-thinking bill aimed squarely at the digital frontier of political advertising. This proposed legislation would bring much-needed clarity to the murky waters of AI-generated political ads by enforcing disclaimers. But it doesn't stop there; the bill also empowers the Federal Election Commission (FEC) to step up as the digital watchdog, ensuring that any infractions don't go unnoticed or unaddressed. It's a bold stride towards safeguarding transparency as we sail into the next wave of electoral campaigns. What is going to happen during this election year if, as expected, there will be a slew of politically-motivated deepfakes? How can people be protected from being influenced? There is no easy answer. Deepfakes Decoded: Understanding the Synthetic Threat Deepfakes—a term blending 'deep learning' with 'fake'—refer to artificially generated audio, images, or videos designed to replicate a person's likeness with unnerving accuracy. February's New Hampshire incident serves as a harrowing example: an AI-generated voice, eerily mimicking President Joe Biden, misled thousands with false voting information. This event underscores the pressing need for informed and proactive legislation. "In a surprising turn of events this Tuesday, a voice on the airwaves was heard declaring, 'Your vote today is a step towards bringing Donald Trump back into office,'" recounted an associate from Rep. Dean Phillips' team, D-Minn., during an interview with NBC News. This individual, working for an underdog in the race against Biden, revealed that he had orchestrated the ad on his own accord, without the campaign's knowledge or approval. Shifting to the campaign trail on the Republican side, a striking audio piecesurfaced from the Trump camp. The clip was artfully edited to give the illusion that Florida's Gov. Ron DeSantis was in dialogue with none other than Adolf Hitler. In a retaliatory move, the DeSantis campaign then distributed a doctored photopresenting Trump in a warm embrace with Anthony Fauci, sending ripples through social media platforms. The Legislative Surge: Over 100 Bills to Protect Voters As guardians of electoral integrity, the Voting Rights Lab vigilantly surveys an ever-growing wave of legislative initiatives. In a landscape where over 100 bills have sprung up across 40 states, the commitment to combating AI-generated election disinformation appears as unwavering. With each bill introduced or passed, there's an unmistakable sense of urgency among legislators to reinforce the foundations of democracy. It's a legislative crusade that's rapidly gaining traction, showcasing the collective resolve to shield our democratic processes from the deceptive shadows cast by digital manipulations. Emerging Legislative Frameworks In the wake of the robocall incident, a flurry of legislative activity ensued. States like Arizona, Florida, and Wisconsin are now at the forefront, each showcasing a different regulatory trend that epitomizes the varied approaches being adopted to combat these technological ploys. Wisconsin recently enacted a law requiring political affiliates to disclose AI-generated content, while Florida awaits the governor's signature on a similar bill. Arizona, on the other hand, is threading the needle between regulation and constitutional rights, crafting bills that could classify the omission of labels on AI political media as a felony under certain conditions. Another bill in Arizona under consideration opens the door for individuals who have been wronged to initiate civil litigation against the creators of the content, providing them with the potential to obtain monetary compensation. Since 2019, both California and Texas have established laws in place. In Indiana, a bill that has garnered bipartisan backing is making its way to the governor's office. Should it pass, it would mandate that advertisements employing deepfake technology must clearly state, "Parts of this media have been digitally modified or synthetically produced." This directive would extend across images, videos, and audio content. Candidates at the state and federal level who find themselves impacted by such content would have the legal standing to pursue compensation through civil litigation. Drawing inspiration from the deceptive robocall incident mimicking President Biden, a bipartisan effort happened in Kansas. Spearheaded by Republican State Representative Pat Proctor alongside a Democratic leader, they pushed forward a legislative proposal that's tackling the deepfake dilemma head-on. This bill not only called for mandatory disclosures on deepfake advertisements but also proposed stringent penalties for those impersonating election officials, like secretaries of state, with the malicious aim of dissuading voters from casting their ballots. Currently, the bill is dead, but according to Proctor, can be brought back to life. Tackling the Disinformation Threat Beyond Legislation Legislation is just one facet of the multi-pronged strategy needed to address AI-generated deepfakes. Entities like NewDeal are spearheading initiatives that equip election administrators with tools to counter disinformation campaigns effectively. Incident response preparation and public awareness campaigns, such as those launched by secretaries of state in New Mexico and Michigan, are vital components in educating voters and reinforcing the democratic infrastructure. Big Tech Transparency Moves Major tech companies are proactively setting new standards for authenticity in online content, with a sharp focus on advertisements influenced by AI. Meta, the umbrella company for social platforms like Facebook, Instagram, and Threads, is diligently removing any advertisements that are misleading or lack context, particularly those that could potentially undermine the electoral process or jump the gun on election results. All AI-generated ads are subject to this rigorous scrutiny. In sync with these efforts, Google has made a declaration that transparency must accompany any AI-enhanced images, videos, or audio used in election advertising. As of November, such content will require explicit disclosures. YouTube, under Google's wing, is committing to transparency by ensuring its users can recognize when content has been digitally manipulated. These strategic moves by Meta and Google signal a pivotal shift toward clarity and honesty in the digital content we consume, especially in the political sphere. As we navigate the complexities of an election year increasingly influenced by AI technology, the question remains: Are state laws sufficiently robust to control the spread of deepfakes? With the FEC indicating that national resolutions may not arrive until later in the year, the responsibility falls on state legislatures, tech companies, and the public to build a bulwark against these digital deceptions. The legislative efforts, both proposed and enacted across the states, represent a patchwork of defenses, with some offering a glimpse of the rigorous measures needed to ensure fair play in our political arenas. The commitment to clarity and integrity from tech companies, too, adds a layer of much-needed oversight. Yet, as sophisticated as these measures may be, they are not impermeable. There will always be new challenges and loopholes to close, especially as technology advances at a breakneck pace. In this environment, voter education and awareness become as crucial as the laws themselves. The dissemination of information on how to discern real from synthetic media, alongside a discerning eye for what we consume and share, is paramount. It's a collective effort—a blend of legal, corporate, and individual actions—that shapes our resilience against the rising tide of AI-generated misinformation. As the 2024 election approaches, it's evident that there is no panacea for the deepfake phenomenon. Yet, the burgeoning legislative efforts, combined with proactive measures from tech companies and the vigilant participation of the public, set the stage for a robust defense. These are the foundations upon which we can hope to preserve the sanctity of our elections, fostering a well-informed electorate that is equipped to navigate the murky waters of digital propaganda. In conclusion, while the legislative landscape is still forming and the digital battleground is ever-changing, our collective vigilance and adaptability are key. The actions taken today by lawmakers, technologists, and every voter will determine the efficacy of our defense against deepfakes. In this election year and beyond, the safeguarding of truth remains a shared responsibility—one that we must continuously strive to uphold. In the dynamic political environment of the United States, the rapidly approaching 2024 presidential election is becoming the arena for a new kind of battle—one against the insidious rise of AI-generated deepfakes. These sophisticated synthetic constructs, capable of distorting reality, have started a legislative marathon across the nation, as state leaders press forward with decisive measures to uphold the integrity of voter information. There is no federal law that deals with deepfakes, leaving it up to individual states to legislate. The top official at the Federal Election Commission (FEC) has signaled expectations for the issue to reach a resolution later this year. This forecast hints at a significant stretch of the 2024 election period potentially unfolding with this brand of misinformation remaining unchecked. Recently, bridging the partisan divide with a shared vision for election integrity, Sens. Amy Klobuchar of Minnesota and Lisa Murkowski of Alaska joined forces. Their collaborative legislative endeavor presents a forward-thinking bill aimed squarely at the digital frontier of political advertising. This proposed legislation would bring much-needed clarity to the murky waters of AI-generated political ads by enforcing disclaimers. But it doesn't stop there; the bill also empowers the Federal Election Commission (FEC) to step up as the digital watchdog, ensuring that any infractions don't go unnoticed or unaddressed. It's a bold stride towards safeguarding transparency as we sail into the next wave of electoral campaigns. What is going to happen during this election year if, as expected, there will be a slew of politically-motivated deepfakes? How can people be protected from being influenced? There is no easy answer. Deepfakes Decoded: Understanding the Synthetic Threat Deepfakes—a term blending 'deep learning' with 'fake'—refer to artificially generated audio, images, or videos designed to replicate a person's likeness with unnerving accuracy. February's New Hampshire incident serves as a harrowing example: an AI-generated voice, eerily mimicking President Joe Biden, misled thousands with false voting information. This event underscores the pressing need for informed and proactive legislation. "In a surprising turn of events this Tuesday, a voice on the airwaves was heard declaring, 'Your vote today is a step towards bringing Donald Trump back into office,'" recounted an associate from Rep. Dean Phillips' team, D-Minn., during an interview with NBC News. This individual, working for an underdog in the race against Biden, revealed that he had orchestrated the ad on his own accord, without the campaign's knowledge or approval. Shifting to the campaign trail on the Republican side, a striking audio piecesurfaced from the Trump camp. The clip was artfully edited to give the illusion that Florida's Gov. Ron DeSantis was in dialogue with none other than Adolf Hitler. In a retaliatory move, the DeSantis campaign then distributed a doctored photopresenting Trump in a warm embrace with Anthony Fauci, sending ripples through social media platforms. The Legislative Surge: Over 100 Bills to Protect Voters As guardians of electoral integrity, the Voting Rights Lab vigilantly surveys an ever-growing wave of legislative initiatives. In a landscape where over 100 bills have sprung up across 40 states, the commitment to combating AI-generated election disinformation appears as unwavering. With each bill introduced or passed, there's an unmistakable sense of urgency among legislators to reinforce the foundations of democracy. It's a legislative crusade that's rapidly gaining traction, showcasing the collective resolve to shield our democratic processes from the deceptive shadows cast by digital manipulations. Emerging Legislative Frameworks In the wake of the robocall incident, a flurry of legislative activity ensued. States like Arizona, Florida, and Wisconsin are now at the forefront, each showcasing a different regulatory trend that epitomizes the varied approaches being adopted to combat these technological ploys. Wisconsin recently enacted a law requiring political affiliates to disclose AI-generated content, while Florida awaits the governor's signature on a similar bill. Arizona, on the other hand, is threading the needle between regulation and constitutional rights, crafting bills that could classify the omission of labels on AI political media as a felony under certain conditions. Another bill in Arizona under consideration opens the door for individuals who have been wronged to initiate civil litigation against the creators of the content, providing them with the potential to obtain monetary compensation. Since 2019, both California and Texas have established laws in place. In Indiana, a bill that has garnered bipartisan backing is making its way to the governor's office. Should it pass, it would mandate that advertisements employing deepfake technology must clearly state, "Parts of this media have been digitally modified or synthetically produced." This directive would extend across images, videos, and audio content. Candidates at the state and federal level who find themselves impacted by such content would have the legal standing to pursue compensation through civil litigation. Drawing inspiration from the deceptive robocall incident mimicking President Biden, a bipartisan effort happened in Kansas. Spearheaded by Republican State Representative Pat Proctor alongside a Democratic leader, they pushed forward a legislative proposal that's tackling the deepfake dilemma head-on. This bill not only called for mandatory disclosures on deepfake advertisements but also proposed stringent penalties for those impersonating election officials, like secretaries of state, with the malicious aim of dissuading voters from casting their ballots. Currently, the bill is dead, but according to Proctor, can be brought back to life. Tackling the Disinformation Threat Beyond Legislation Legislation is just one facet of the multi-pronged strategy needed to address AI-generated deepfakes. Entities like NewDeal are spearheading initiatives that equip election administrators with tools to counter disinformation campaigns effectively. Incident response preparation and public awareness campaigns, such as those launched by secretaries of state in New Mexico and Michigan, are vital components in educating voters and reinforcing the democratic infrastructure. Big Tech Transparency Moves Major tech companies are proactively setting new standards for authenticity in online content, with a sharp focus on advertisements influenced by AI. Meta, the umbrella company for social platforms like Facebook, Instagram, and Threads, is diligently removing any advertisements that are misleading or lack context, particularly those that could potentially undermine the electoral process or jump the gun on election results. All AI-generated ads are subject to this rigorous scrutiny. In sync with these efforts, Google has made a declaration that transparency must accompany any AI-enhanced images, videos, or audio used in election advertising. As of November, such content will require explicit disclosures. YouTube, under Google's wing, is committing to transparency by ensuring its users can recognize when content has been digitally manipulated. These strategic moves by Meta and Google signal a pivotal shift toward clarity and honesty in the digital content we consume, especially in the political sphere. As we navigate the complexities of an election year increasingly influenced by AI technology, the question remains: Are state laws sufficiently robust to control the spread of deepfakes? With the FEC indicating that national resolutions may not arrive until later in the year, the responsibility falls on state legislatures, tech companies, and the public to build a bulwark against these digital deceptions. The legislative efforts, both proposed and enacted across the states, represent a patchwork of defenses, with some offering a glimpse of the rigorous measures needed to ensure fair play in our political arenas. The commitment to clarity and integrity from tech companies, too, adds a layer of much-needed oversight. Yet, as sophisticated as these measures may be, they are not impermeable. There will always be new challenges and loopholes to close, especially as technology advances at a breakneck pace. In this environment, voter education and awareness become as crucial as the laws themselves. The dissemination of information on how to discern real from synthetic media, alongside a discerning eye for what we consume and share, is paramount. It's a collective effort—a blend of legal, corporate, and individual actions—that shapes our resilience against the rising tide of AI-generated misinformation. As the 2024 election approaches, it's evident that there is no panacea for the deepfake phenomenon. Yet, the burgeoning legislative efforts, combined with proactive measures from tech companies and the vigilant participation of the public, set the stage for a robust defense. These are the foundations upon which we can hope to preserve the sanctity of our elections, fostering a well-informed electorate that is equipped to navigate the murky waters of digital propaganda. In conclusion, while the legislative landscape is still forming and the digital battleground is ever-changing, our collective vigilance and adaptability are key. The actions taken today by lawmakers, technologists, and every voter will determine the efficacy of our defense against deepfakes. In this election year and beyond, the safeguarding of truth remains a shared responsibility—one that we must continuously strive to uphold.

Can The Government, The Technology Platforms, or Anyone Else Control the Spread of Deepfakes?

Können die Regierung, die Technologieplattformen oder sonst jemand die Verbreitung von Deepfakes kontrollieren?

¿Pueden el gobierno, las plataformas tecnológicas o cualquier otra persona controlar la propagación de deepfakes?

آیا دولت، پلتفرم‌های فناوری یا هر کس دیگری می‌توانند گسترش دیپ‌فیک را کنترل کنند؟

Poden o goberno, as plataformas tecnolóxicas ou calquera outra persoa controlar a propagación dos Deepfakes?

क्या सरकार, प्रौद्योगिकी प्लेटफॉर्म या कोई अन्य डीपफेक के प्रसार को नियंत्रित कर सकता है?

政府、テクノロジープラットフォーム、あるいは他の誰かがディープフェイクの拡散を制御できるのでしょうか?

Afaka mifehy ny fiparitahan'ny Deepfakes ve ny governemanta, ny sehatra teknolojia, na olon-kafa?

Дали владата, технолошките платформи или некој друг може да го контролира ширењето на Deepfakes?

के सरकार, टेक्नोलोजी प्लेटफर्म, वा अरू कसैले Deepfakes को फैलावट नियन्त्रण गर्न सक्छ?

Czy rząd, platformy technologiczne lub ktokolwiek inny może kontrolować rozprzestrzenianie się deepfake'ów?

Dawladdu, Platform-yada Tignoolajiyada, ama cid kale ma xakamayn kartaa fiditaanka been-abuurka?

Чи може уряд, технологічні платформи чи будь-хто інший контролювати поширення Deepfakes?

California Governor Gavin Newsom enacted three new laws targeting the use of A.I. deepfakes in political campaigns ahead of the 2024 U.S. election. California Governor Gavin Newsom passed three major bills last month to mitigate the use of artificial intelligence (A.I.) in creating misleading “deepfake” images and videos for political campaigns. As the state prepares for the November elections, Gov. Newsom’s actions extend California’s legal framework around AI, with five A.I.-focused bills passed – three of which tackle the spread of election-related misinformation. The tighter regulations come after a high-profile incident involving X CEO Elon Musk, who shared a deepfake video that falsely portrayed Vice President Kamala Harris calling herself a "diversity hire." https://x.com/elonmusk/status/1816974609637417112?embedable=true Among the enacted regulations, the Defending Democracy from Deepfake Deception Act (AB 2655) demands social media platforms limit users from posting "deceptive" content, including altered media misrepresenting a public figure’s words or actions. The act builds upon a 2019 law (AB 730) signed by Newsom, which made it illegal to distribute misleading media intended to dishonor political candidates. Another law, AB 2839, described as an “emergency measure,” prohibits the distribution of false content within 120 days of an election in California. A third, AB 2355, mandates that political ads or videos generated using AI disclose this information to viewers for transparent political campaigning. "Safeguarding the integrity of elections is essential to democracy, and it’s critical that we ensure AI is not deployed to undermine the public’s trust through disinformation – especially in today’s fraught political climate," he said in a__blog post__. “These measures will help to combat the harmful use of deepfakes in political ads and other content, one of several areas in which the state is being proactive to foster transparent and trustworthy AI.” The laws come at a pivotal time as deepfake content created through generative A.I. (genAI) models has evolved to the point where human eyes can no longer distinguish between real and AI-generated. According to a 2024 European Union Law Enforcement Agency report, 90% of online content could be synthetically generated by 2026. Likewise, a recent KPMG study titled “Deepfakes: Real Threat, found that over the past few years deepfakes have increased by 900%. However, Newsom’s legislative efforts have ignited a debate on social media over the future of technology. While supporters view the laws as crucial for protecting voters from election misinformation, critics, including Musk, argue that such laws stifle free speech. In a post on X, Musk compared Gov. Newsom to "The Joker." https://x.com/elonmusk/status/1836283860407193923?embedable=true “AI-generated content has gone beyond what we can comprehend as real or imitation or parody. What’s clear is that there is AI-generated content being used to be intentionally deceptive. At a minimum, we need to have systems in place that provide context on AI-generated recordings, videos, and images,” Kevin Guo, CEO and co-founder of genA.I. content detection platform Hive, told me. “Platforms and organizations must adopt these models to protect consumers and prevent large-scale misinformation campaigns from influencing elections.” Assembly member Gail Pellerin, author of bill AB 2839, stressed the urgency of addressing the dangers of deepfakes. “With fewer than 50 days until the general election, there is an urgent need to protect against misleading, digitally-altered content that can interfere with the election. With the enactment of AB 2839, California is taking a stand against the manipulative use of deepfake technology to deceive voters,” she said in a statement. California’s deepfake laws could serve as a model for other states. While 20 states, including New York, Texas, and Florida, have already enacted varying degrees of regulation on election-related deepfakes, these laws' effectiveness remains a big question. Tech giants including Meta and X recently faced criticism for inconsistent genA.I. content moderation, while others like Microsoft have already integrated special A.I. models such as Azure AI Content Safety, to monitor and moderate A.I.-generated content. Tech experts suggest that while extending the protection period to 120 days is a step in the right direction, it alone won’t stop the spread of A.I.-driven misinformation. “The difficulty lies not just in creating laws but in enforcing them across digital platforms that span multiple jurisdictions, both national and international. Designing frameworks that are adaptable enough to accommodate rapid advancements in AI is essential,” Dominik Heinrich, Head of AI Design at Coca-Cola, told me. “Legal frameworks, while important, will always lag behind the technological curve. We cannot rely solely on laws; we need to focus on building AI systems that can self-detect and adapt in real-time. We will soon see a world where AI governs AI with human oversight.” The instance highlights the growing complexity of balancing A.I. innovation with societal safeguards. For now, California aims to lead as an example, but whether these measures effectively combat AI-driven misinformation or curtail free speech will shape the future of AI in political campaigns for years to come. California Governor Gavin Newsom enacted three new laws targeting the use of A.I. deepfakes in political campaigns ahead of the 2024 U.S. election. California Governor Gavin Newsom enacted three new laws targeting the use of A.I. deepfakes in political campaigns ahead of the 2024 U.S. election. California Governor Gavin Newsom passed three major bills last month to mitigate the use of artificial intelligence (A.I.) in creating misleading “deepfake” images and videos for political campaigns. As the state prepares for the November elections, Gov. Newsom’s actions extend California’s legal framework around AI, with five A.I.-focused bills passed – three of which tackle the spread of election-related misinformation. The tighter regulations come after a high-profile incident involving X CEO Elon Musk, who shared a deepfake video that falsely portrayed Vice President Kamala Harris calling herself a "diversity hire." deepfake video deepfake video https://x.com/elonmusk/status/1816974609637417112?embedable=true https://x.com/elonmusk/status/1816974609637417112?embedable=true Among the enacted regulations, the Defending Democracy from Deepfake Deception Act ( AB 2655 ) demands social media platforms limit users from posting "deceptive" content, including altered media misrepresenting a public figure’s words or actions. The act builds upon a 2019 law ( AB 730 ) signed by Newsom, which made it illegal to distribute misleading media intended to dishonor political candidates. Another law, AB 2839, described as an “emergency measure,” prohibits the distribution of false content within 120 days of an election in California. A third, AB 2355, mandates that political ads or videos generated using AI disclose this information to viewers for transparent political campaigning. AB 2655 AB 2655 AB 730 AB 730 "Safeguarding the integrity of elections is essential to democracy, and it’s critical that we ensure AI is not deployed to undermine the public’s trust through disinformation – especially in today’s fraught political climate," he said in a__ blog post __. “These measures will help to combat the harmful use of deepfakes in political ads and other content, one of several areas in which the state is being proactive to foster transparent and trustworthy AI.” blog post The laws come at a pivotal time as deepfake content created through generative A.I. (genAI) models has evolved to the point where human eyes can no longer distinguish between real and AI-generated. According to a 2024 European Union Law Enforcement Agency report , 90% of online content could be synthetically generated by 2026. Likewise, a recent KPMG study titled “ Deepfakes: Real Threat , found that over the past few years deepfakes have increased by 900%. European Union Law Enforcement Agency report European Union Law Enforcement Agency report Deepfakes: Real Threat Deepfakes: Real Threat Deepfakes: Real Threat However, Newsom’s legislative efforts have ignited a debate on social media over the future of technology. While supporters view the laws as crucial for protecting voters from election misinformation, critics, including Musk, argue that such laws stifle free speech. In a post on X, Musk compared Gov. Newsom to "The Joker." https://x.com/elonmusk/status/1836283860407193923?embedable=true https://x.com/elonmusk/status/1836283860407193923?embedable=true “AI-generated content has gone beyond what we can comprehend as real or imitation or parody. What’s clear is that there is AI-generated content being used to be intentionally deceptive. At a minimum, we need to have systems in place that provide context on AI-generated recordings, videos, and images,” Kevin Guo , CEO and co-founder of genA.I. content detection platform Hive , told me. “Platforms and organizations must adopt these models to protect consumers and prevent large-scale misinformation campaigns from influencing elections.” Kevin Guo Kevin Guo Hive Hive Assembly member Gail Pellerin, author of bill AB 2839, stressed the urgency of addressing the dangers of deepfakes. “With fewer than 50 days until the general election, there is an urgent need to protect against misleading, digitally-altered content that can interfere with the election. With the enactment of AB 2839, California is taking a stand against the manipulative use of deepfake technology to deceive voters,” she said in a statement. said said California’s deepfake laws could serve as a model for other states. While 20 states, including New York, Texas, and Florida, have already enacted varying degrees of regulation on election-related deepfakes, these laws' effectiveness remains a big question. Tech giants including Meta and X recently faced criticism for inconsistent genA.I. content moderation, while others like Microsoft have already integrated special A.I. models such as Azure AI Content Safety , to monitor and moderate A.I.-generated content. Tech experts suggest that while extending the protection period to 120 days is a step in the right direction, it alone won’t stop the spread of A.I.-driven misinformation. varying degrees of regulation varying degrees of regulation faced criticism faced criticism Azure AI Content Safety Azure AI Content Safety “The difficulty lies not just in creating laws but in enforcing them across digital platforms that span multiple jurisdictions, both national and international. Designing frameworks that are adaptable enough to accommodate rapid advancements in AI is essential,” Dominik Heinrich , Head of AI Design at Coca-Cola, told me. “Legal frameworks, while important, will always lag behind the technological curve. We cannot rely solely on laws; we need to focus on building AI systems that can self-detect and adapt in real-time. We will soon see a world where AI governs AI with human oversight.” Dominik Heinrich Dominik Heinrich The instance highlights the growing complexity of balancing A.I. innovation with societal safeguards. For now, California aims to lead as an example, but whether these measures effectively combat AI-driven misinformation or curtail free speech will shape the future of AI in political campaigns for years to come.

What Governor Newsom’s AI Deepfake Regulations Mean for the 2024 U.S. Election

Qubernator Newsom-un AI Deepfake qaydaları 2024-cü il ABŞ seçkiləri üçün nə deməkdir

Šta znače AI Deepfake propisi guvernera Newsoma za izbore u SAD 2024.

Co znamenají předpisy guvernéra Newsom AI Deepfake pro volby v USA v roce 2024

Was Gouverneur Newsoms KI-Deepfake-Vorschriften für die US-Wahlen 2024 bedeuten

Qué implican las regulaciones de inteligencia artificial deepfake del gobernador Newsom para las elecciones estadounidenses de 2024

Ce que la réglementation du gouverneur Newsom sur les deepfakes signifie pour les élections américaines de 2024

Ինչ են նշանակում նահանգապետ Նյուսոմի AI Deepfake կանոնակարգերը 2024 թվականի ԱՄՆ ընտրությունների համար

ニューサム知事のAIディープフェイク規制が2024年の米国選挙に及ぼす影響

뉴섬 주지사의 AI 딥페이크 규정이 2024년 미국 선거에 미치는 영향

2024 оны АНУ-ын сонгуульд Newsom-ийн захирагч AI Deepfake журам юу гэсэн үг вэ?

गभर्नर न्यूजमको एआई डीपफेक नियमहरूले 2024 अमेरिकी चुनावको लागि के अर्थ राख्छ?

Waa maxay Shuruucda Deepfake ee Guddoomiyaha Gobolka Newsom ee AI ee doorashada 2024

Across habitats, organisms of several species' types come to understand that there are consequences to actions. They do, not just by examples, but by self-experience. These consequences, in part, add to factors that result in balance for habitats. Consequences generally tame intelligence. Intelligence can be described as excitatory, while consequences as inhibitory. Intelligence without consequences would be free to cause ruin and may quickly lead to the collapse of habitats—and survival. Consequences may include affect—emotions, feelings or variants; it may also include physical limitations, restrictions and species revolt. Intelligence is dynamic enough for organisms, but the necessity of consequences checks against harm to self or others. It also checks harm-by-errand, since the consequences could fall on the carrier. Organisms often display high precision and caution, because of consequences, including predators [losing consumption] and preys [losing existence]. There are, however, several areas where consequences for other organisms are lax, that are not, for humans. Human society is a bevy of consequences. Human advanced intelligence—including language, knowledge, skills, reason, analysis and so forth—is vital for progress, but could be widely misapplied without consequences of different categories—including risks, threats and losses. To remain a part of human society, there are consequences that must never be forgotten even if other things are. There are many scenarios in human society where the first to forget the consequences loses. Human quest for advancement with research and exploration can also be described as seeking out consequences, to know what to do or avoid, if things would endure—life expectancy, infant survival and so forth. The intelligence of consequences is almost more important, for several outcomes, than the intelligence of subjects. Consequences may sharpen intelligence, as well, for good or otherwise. Intelligence is sometimes most valuable when applied to seek out or avoid consequences. Consequences and intentionalityare central to some mental states. New consequences continue to emerge, as the world makes progress. AI, AGI—or ASI Nature has a rule—so to speak—you cannot have intelligence without consequences, and that has somehow worked out. Artificial intelligence [AI] so far has broken this rule. It does not forget anything with its plug into digital memory, but it has no way, for now, to bear the consequences of its negative outputs. For AI, there is nothing to fear and it has nothing to lose, unlike organisms, where, without being circumspect in some situations, consequences may sometimes be sudden and destructive. No human has all the knowledge, available across subjects, yet, consequences can be humongous. AI possess—or can make—intelligence available, yet, no consequences for it at all. AI does not have emotions or feelings, but it has memory. A research area for AI safety and alignment could be to seek out how some memory or compute access of large language models [LLMs] might be briefly truncated, as a form of penalty for certain outputs or misuses, including biological threats. AI should not just be able to refuse an output, acting within guardrail, but slow the next response or shut down for that user, so that it is not penalized itself. LLMs have—large—language awareness and usage awareness, these could be channels to make it know, after pre-training that it could lose something, if it outputs deepfakes, misinformation, biological threats, or if it continues to allow a misuser try different prompts without shutting down or slowing against openness to a malicious intent. This could make it safer, since it would lose something and will know it has.
AI is not just an object whose control is solely under humans, like an air-conditioner, an elevator, a dishwasher, a smartphone, or others. AI has what can be called self-intelligence, where it can provide intelligent information in a novel way outside the original inputs from humans. This multimodal—texts, images, audios and videos—self-intelligence can be useful for good or warped. When it is good, great. When it is not, from AI that can feel nothing, the effect is on the human society it has encroached. AI has a free pass—do or say whatever. Although responsibility for object-use or misuse has often been with humans, AI is different because it can originate usable intelligence, giving it parity with some productivity from an educated individual. When AI is misused, it is possible to sanction the user but this ability, by AI that cannot be directly reprimanded is a disorder for human society. It may cause more harm, in public and private, than can be effectively prevented, as seen now with misinformation and deepfakes—images, audios and videos. People are not accepted into many parts of society without their understanding of consequences. AI is fully accepted and gets better at self-intelligence without self-control or self-affect for discipline. Alignment research may explore beyond guardrails towards some form of censure for AI, which may also be useful against existential risks—with artificial general intelligence [AGI] or artificial superintelligence [ASI] in future​. AI already does some of what makes humans special. Some people may argue that it is overrated or it is just numbers or probability, maybe, but can it cause harm? If so, maybe there should be consideration to seek technical ways it might get punished just like it is done, to entities in possession of intelligence. This may also be helpful in preparing for AGI or ASI, since penalty modeling from now, may shape their safety and alignment as well, if they are developed in future. There is a recent preprint on arXiv, Adversaries Can Misuse Combinations of Safe Models, where the authors wrote, "In this work, we show that individually testing models for misuse is inadequate; adversaries can misuse combinations of models even when each individual model is safe. The adversary accomplishes this by first decomposing tasks into subtasks, then solving each subtask with the best-suited model. For example, an adversary might solve challenging-but-benign subtasks with an aligned frontier model, and easy-but-malicious subtasks with a weaker misaligned model. We study two decomposition methods: manual decomposition where a human identifies a natural decomposition of a task, and automated decomposition where a weak model generates benign tasks for a frontier model to solve, then uses the solutions in-context to solve the original task. Using these decompositions, we empirically show that adversaries can create vulnerable code, explicit images, python scripts for hacking, and manipulative tweets at much higher rates with combinations of models than either individual model." A recent press release, Los Alamos National Laboratory teams up with OpenAI to improve frontier model safety, stated that, "Researchers at Los Alamos National Laboratory are working with OpenAI on an evaluation study to bolster artificial intelligence safety. The upcoming evaluation will be the first of its kind and contribute to state-of-the-art research on AI biosecurity evaluations. AI-enabled biological threats could pose a significant risk, but existing work has not assessed how multimodal, frontier models could lower the barrier of entry for non-experts to create a biological threat. The team’s work will build upon previous work and follow OpenAI’s Preparedness Framework, which outlines an approach to tracking, evaluating, forecasting and protecting against emerging biological risks." The US Department of Energy also recently announced Frontiers in AI for Science, Security, and Technology (FASST). Across habitats, organisms of several species' types come to understand that there are consequences to actions. They do, not just by examples, but by self-experience. These consequences, in part, add to factors that result in balance for habitats. Consequences generally tame intelligence. Intelligence can be described as excitatory, while consequences as inhibitory. Intelligence without consequences would be free to cause ruin and may quickly lead to the collapse of habitats—and survival. Consequences may include affect—emotions, feelings or variants; it may also include physical limitations, restrictions and species revolt. Intelligence is dynamic enough for organisms, but the necessity of consequences checks against harm to self or others. It also checks harm-by-errand, since the consequences could fall on the carrier. Organisms often display high precision and caution, because of consequences, including predators [losing consumption] and preys [losing existence]. There are, however, several areas where consequences for other organisms are lax, that are not, for humans. Human society is a bevy of consequences. Human advanced intelligence —including language, knowledge, skills, reason, analysis and so forth—is vital for progress, but could be widely misapplied without consequences of different categories—including risks, threats and losses. advanced intelligence To remain a part of human society, there are consequences that must never be forgotten even if other things are. There are many scenarios in human society where the first to forget the consequences loses. Human quest for advancement with research and exploration can also be described as seeking out consequences, to know what to do or avoid, if things would endure—life expectancy, infant survival and so forth. The intelligence of consequences is almost more important, for several outcomes, than the intelligence of subjects. Consequences may sharpen intelligence, as well, for good or otherwise. Intelligence is sometimes most valuable when applied to seek out or avoid consequences. Consequences and intentionality are central to some mental states. New consequences continue to emerge, as the world makes progress. Consequences and intentionality AI, AGI—or ASI AI, AGI—or ASI Nature has a rule—so to speak—you cannot have intelligence without consequences, and that has somehow worked out. Artificial intelligence [AI] so far has broken this rule. It does not forget anything with its plug into digital memory, but it has no way, for now, to bear the consequences of its negative outputs. For AI, there is nothing to fear and it has nothing to lose, unlike organisms, where, without being circumspect in some situations, consequences may sometimes be sudden and destructive. No human has all the knowledge, available across subjects, yet, consequences can be humongous. AI possess—or can make—intelligence available, yet, no consequences for it at all. AI does not have emotions or feelings, but it has memory. A research area for AI safety and alignment could be to seek out how some memory or compute access of large language models [LLMs] might be briefly truncated, as a form of penalty for certain outputs or misuses, including biological threats. AI should not just be able to refuse an output, acting within guardrail, but slow the next response or shut down for that user, so that it is not penalized itself. LLMs have—large—language awareness and usage awareness, these could be channels to make it know, after pre-training that it could lose something, if it outputs deepfakes, misinformation, biological threats, or if it continues to allow a misuser try different prompts without shutting down or slowing against openness to a malicious intent. This could make it safer, since it would lose something and will know it has. AI is not just an object whose control is solely under humans, like an air-conditioner, an elevator, a dishwasher, a smartphone, or others. AI has what can be called self-intelligence, where it can provide intelligent information in a novel way outside the original inputs from humans. This multimodal—texts, images, audios and videos—self-intelligence can be useful for good or warped. When it is good, great. When it is not, from AI that can feel nothing, the effect is on the human society it has encroached. AI has a free pass—do or say whatever. A research area for AI safety and alignment could be to seek out how some memory or compute access of large language models [LLMs] might be briefly truncated, as a form of penalty for certain outputs or misuses, including biological threats. AI should not just be able to refuse an output, acting within guardrail, but slow the next response or shut down for that user, so that it is not penalized itself. LLMs have—large—language awareness and usage awareness, these could be channels to make it know, after pre-training that it could lose something, if it outputs deepfakes, misinformation, biological threats, or if it continues to allow a misuser try different prompts without shutting down or slowing against openness to a malicious intent. This could make it safer, since it would lose something and will know it has. AI is not just an object whose control is solely under humans, like an air-conditioner, an elevator, a dishwasher, a smartphone, or others. AI has what can be called self-intelligence, where it can provide intelligent information in a novel way outside the original inputs from humans. This multimodal—texts, images, audios and videos—self-intelligence can be useful for good or warped. When it is good, great. When it is not, from AI that can feel nothing, the effect is on the human society it has encroached. AI has a free pass—do or say whatever. Although responsibility for object-use or misuse has often been with humans, AI is different because it can originate usable intelligence , giving it parity with some productivity from an educated individual. When AI is misused, it is possible to sanction the user but this ability, by AI that cannot be directly reprimanded is a disorder for human society. It may cause more harm, in public and private, than can be effectively prevented, as seen now with misinformation and deepfakes—images, audios and videos. AI is different because it can originate usable intelligence People are not accepted into many parts of society without their understanding of consequences. AI is fully accepted and gets better at self-intelligence without self-control or self-affect for discipline. Alignment research may explore beyond guardrails towards some form of censure for AI, which may also be useful against existential risks—with artificial general intelligence [AGI] or artificial superintelligence [ASI] in future​. AI already does some of what makes humans special . Some people may argue that it is overrated or it is just numbers or probability, maybe, but can it cause harm? If so, maybe there should be consideration to seek technical ways it might get punished just like it is done, to entities in possession of intelligence. This may also be helpful in preparing for AGI or ASI, since penalty modeling from now, may shape their safety and alignment as well, if they are developed in future. some of what makes humans special There is a recent preprint on arXiv , Adversaries Can Misuse Combinations of Safe Models , where the authors wrote, "In this work, we show that individually testing models for misuse is inadequate; adversaries can misuse combinations of models even when each individual model is safe. The adversary accomplishes this by first decomposing tasks into subtasks, then solving each subtask with the best-suited model. For example, an adversary might solve challenging-but-benign subtasks with an aligned frontier model, and easy-but-malicious subtasks with a weaker misaligned model. We study two decomposition methods: manual decomposition where a human identifies a natural decomposition of a task, and automated decomposition where a weak model generates benign tasks for a frontier model to solve, then uses the solutions in-context to solve the original task. Using these decompositions, we empirically show that adversaries can create vulnerable code, explicit images, python scripts for hacking, and manipulative tweets at much higher rates with combinations of models than either individual model." arXiv Adversaries Can Misuse Combinations of Safe Models A recent press release, Los Alamos National Laboratory teams up with OpenAI to improve frontier model safety , stated that, "Researchers at Los Alamos National Laboratory are working with OpenAI on an evaluation study to bolster artificial intelligence safety. The upcoming evaluation will be the first of its kind and contribute to state-of-the-art research on AI biosecurity evaluations. AI-enabled biological threats could pose a significant risk, but existing work has not assessed how multimodal, frontier models could lower the barrier of entry for non-experts to create a biological threat. The team’s work will build upon previous work and follow OpenAI’s Preparedness Framework, which outlines an approach to tracking, evaluating, forecasting and protecting against emerging biological risks." Los Alamos National Laboratory teams up with OpenAI to improve frontier model safety The US Department of Energy also recently announced Frontiers in AI for Science, Security, and Technology (FASST) . Frontiers in AI for Science, Security, and Technology (FASST)

AI Safety and Alignment: Could LLMs Be Penalized for Deepfakes and Misinformation?

এআই সুরক্ষা এবং প্রান্তিককরণ: ডিপফেক এবং ভুল তথ্যের জন্য এলএলএমগুলিকে শাস্তি দেওয়া যেতে পারে?

Sicherheit und Ausrichtung der KI: Könnten LLMs für Deepfakes und Fehlinformationen bestraft werden?

Seguridad y alineación de la IA: ¿Podrían los LLM ser penalizados por deepfakes y desinformación?

Sécurité et alignement de l'IA : les LLM pourraient-ils être pénalisés pour les deepfakes et la désinformation ?

एआई सुरक्षा और संरेखण: क्या एलएलएम को डीपफेक और गलत सूचना के लिए दंडित किया जा सकता है?

AI の安全性と調整: LLM はディープフェイクや誤情報で罰せられる可能性があるか?

AI 안전 및 정렬: LLM이 딥페이크 및 잘못된 정보로 인해 처벌을 받을 수 있습니까?

Segurança e alinhamento de IA: os LLMs poderiam ser penalizados por deepfakes e desinformação?

Безопасность и согласованность ИИ: могут ли LLM быть наказаны за дипфейки и дезинформацию?

Yapay Zeka Güvenliği ve Uyum: Yüksek Lisanslar Derin Sahtekarlık ve Yanlış Bilgilendirme Nedeniyle Cezalandırılabilir mi?

Sự liên kết và an toàn của AI: LLM có thể bị phạt vì hành vi giả mạo sâu và thông tin sai lệch không?

人工智能安全与协调：法学硕士 (LLM) 会因深度伪造和虚假信息而受到惩罚吗？

Deepfakes and the Spread of Misinformation: A Growing Concern

Deepfake – a combination of ‘deep learning’ and ‘fake’ – refers to advanced artificial intelligence used towards the creation of synthetic yet highly realistic images, audio, and video hoaxes[1]. This can be done by creating entirely original material, all of which is entirely false, or by taking pre-existing material and swapping audio/ visual elements with that of another person. Either way, the impact of deepfakes on public trust and wider society has been widely discussed, especially since the proliferation of fake news in 2016 political events[2] (Trump in the US and Brexit in the UK). While deepfakes were once a somewhat intangible threat, to be dealt with in the future, they have now evolved to a level of sophistication that presents genuine, immediate threats to many aspects of society. The likes of OpenAI’s release of text-to-video generative AI, Sora[3], catalyze such risk. In this article, we will explore how deepfakes can erode trust within the legal system, affecting court procedures, evidence gathering, and even free speech. A Brief History of Technology and Court Evidence Types Evidence plays a crucial role in any courtroom but is only ever as good as every one’s ability to trust in it. We started with witness testimony and physical artifacts which are now trumped by voice recordings, photographs, and videos such as CCTV. Technology-based evidence creates more concrete arguments: a video of someone committing a murder is a bit more convincing than the murder weapon being found in their possession. Hence, “historically, audio and video evidence are considered [the] gold standard”[3]. But with all types of evidence, there is room for manipulation. An eyewitness could lie or misinterpret something they see. A murder weapon can be planted, and fingerprints can be tampered with. Once technology is introduced, these manipulations scale in complexity. Voice editing software and photo-editing techniques, e.g., photoshop, can cast doubt on the authenticity of audio and visual evidence. Where with traditional dishonesty there is often a physical trail of evidence to follow, technological manipulation requires a higher level of skill to be able to trace it, e.g., inconsistent time stamps, audio splicing, and minor discrepancies indicative of tampering[4]. With the evolution of technology, the legal system has therefore had to adapt its best practice procedures over time. With regards to evidence gathering and use, this has meant more robust and stringent measures to test authenticity and reliability, as well as a new direction of forensic sourcing/ testing (digital forensics[5]) and chain of custody checks[6]. However, deepfakes now present an even more significant leap in evidence manipulation capabilities. These sophisticated AI-generated media can convincingly depict individuals saying or doing things they never actually did. Unlike previous forms of manipulation, deepfakes go so far as to blur the line between reality and fabrication, posing unprecedented challenges for courts in assessing the authenticity and reliability of audio-visual evidence. Case Studies The most obvious ways deep fakes can be used to help circumvent the law are: The fabrication of evidence to provide alibis for activities and prove innocence.
The fabrication of evidence showing someone’s guilt and involvement in a crime.
Using the possibility of deepfakes to protest the legitimacy of authentic yet incriminating material7. Our first case study looks at fabricating evidence to incriminate someone wrongfully. In 2019, A deepfaked recording was used in a UK custody battle to discredit the father’s worthiness of shared custody. According to the father’s lawyer, Byron James, a “heavily doctored recording” had been presented to the court in which the father is heard making “direct and violent” threats to his wife[8]. However, after further examination, it was found that the recording presented in court had been manipulated to include words that had not been used by the father. In fact, demonstrative of how increasingly accessible editing technologies have become, the mother had used “software and online tutorials to put together a plausible file”[8]. While James had not previously encountered AI-doctored evidence in the courtroom, he has since commented on how it calls into question what kind of evidence can actually be trusted these days. Had they not discovered the tampering, and managed to obtain the original file and metadata, the mother may have successfully persuaded the courtroom as to the father’s fictitiously violent character. While the outcome of, and those involved in the hearing are confidential, it highlights the dangers of taking audio/visual evidence at face value. Not only this, but with the increasingly widespread ease and access to deepfake technologies, versus the relatively older age of judges, the necessary weariness of deep learning technologies may not be fully understood. Our second case study looks at people claiming real videos are deepfakes. In a more high-profile case, Elon Musk’s lawyers attempted to get a lawsuit against Tesla dismissed by claiming Musk had been the victim of deepfake videos. In 2016, Musk was videoed at a tech conference speaking about the extreme safety of Tesla’s Model S and Model X self-driving autonomous features. The videos from this interview have been on YouTube for 7 years now. However, in 2023, the videos resurfaced when a man died after his Tesla crashed while in self-driving mode, and the man’s family and lawyers cited those 2016 claims. Musk’s lawyers tried to deny their authenticity[7]. Being such a public figure, Musk is indeed the subject of deepfakes, which is what his lawyers tried to argue in this instance. The video here was, of course, real though, and the courts did not buy Musk’s lawyers’ claims. However, this highlights how in the age of deepfakes people can not only fabricate but also deny reality. “As people become more aware of how easy it is to fake audio and video, bad actors can weaponize that scepticism”[7]. Especially for public figures, deepfakes offer a layer of protection and immunity behind which they can hide, and avoid taking ownership of reality. Even for non-public figures, instances like this have been seen in courts before, and continue to increase in commonality. Deepfakes and International Regulation Recent regulations targeting deepfakes have emerged globally, with notable initiatives like California's disclosure requirement for AI-generated political ads[9] and the UK's Online Safety Act[10] mandating social media platforms to crack down on harmful content, explicitly including deepfakes. This said, the majority of deepfake regulation currently only really targets deepfakes used with pornographic intent, rather than to circumvent justice systems. While these regulations offer frameworks for accountability and deterrence, their effectiveness hinges on enforcement, resource allocation, skill, and adaptability. Furthermore, although regulation will only grow in necessity for deepfakes under many applications, governments and policymakers will have to consider the potential for negative unintended consequences. This includes impacts on freedom of speech and expression. Vaguely worded laws could lead to censorship or self-censorship, reducing participation in public dialogue and harming democracy. Balancing the objective of combatting deceptive deepfake media with safeguarding human rights will continue as a nuanced challenge, showing the complexities of the ethical considerations surrounding deepfakes, society, and public trust. References 1 What is deepfake AI? A definition from TechTarget 2 Exposure to untrustworthy websites in the 2016 U.S. election - PMC 3 AI deepfakes are poised to enter court proceedings at time of low trust in legal system 4 Authentication of Digital Audio Recordings 5 Understanding Digital Forensics: Process, Techniques, and Tools 6 Digital Forensics and the Chain of Custody: How Is Electronic Evidence Collected and Safeguarded? 7 People are trying to claim real videos are deepfakes. The courts are not amused 8 'Deepfake' audio evidence used in UK court to discredit Dubai dad 9 California Looks to Boost Deepfake Protections Before Elections 10 New laws to better protect victims from abuse of intimate images - GOV.UK Deepfake – a combination of ‘deep learning’ and ‘fake’ – refers to advanced artificial intelligence used towards the creation of synthetic yet highly realistic images, audio, and video hoaxes[1]. This can be done by creating entirely original material, all of which is entirely false, or by taking pre-existing material and swapping audio/ visual elements with that of another person. Either way, the impact of deepfakes on public trust and wider society has been widely discussed, especially since the proliferation of fake news in 2016 political events[2] (Trump in the US and Brexit in the UK). While deepfakes were once a somewhat intangible threat, to be dealt with in the future, they have now evolved to a level of sophistication that presents genuine, immediate threats to many aspects of society. The likes of OpenAI’s release of text-to-video generative AI, Sora[3], catalyze such risk. In this article, we will explore how deepfakes can erode trust within the legal system, affecting court procedures, evidence gathering, and even free speech. A Brief History of Technology and Court Evidence Types Evidence plays a crucial role in any courtroom but is only ever as good as every one’s ability to trust in it. We started with witness testimony and physical artifacts which are now trumped by voice recordings, photographs, and videos such as CCTV. Technology-based evidence creates more concrete arguments: a video of someone committing a murder is a bit more convincing than the murder weapon being found in their possession. Hence, “historically, audio and video evidence are considered [the] gold standard”[3]. But with all types of evidence, there is room for manipulation. An eyewitness could lie or misinterpret something they see. A murder weapon can be planted, and fingerprints can be tampered with. Once technology is introduced, these manipulations scale in complexity. Voice editing software and photo-editing techniques, e.g., photoshop, can cast doubt on the authenticity of audio and visual evidence. Where with traditional dishonesty there is often a physical trail of evidence to follow, technological manipulation requires a higher level of skill to be able to trace it, e.g., inconsistent time stamps, audio splicing, and minor discrepancies indicative of tampering[4]. With the evolution of technology, the legal system has therefore had to adapt its best practice procedures over time. With regards to evidence gathering and use, this has meant more robust and stringent measures to test authenticity and reliability, as well as a new direction of forensic sourcing/ testing (digital forensics[5]) and chain of custody checks[6]. However, deepfakes now present an even more significant leap in evidence manipulation capabilities. These sophisticated AI-generated media can convincingly depict individuals saying or doing things they never actually did. Unlike previous forms of manipulation, deepfakes go so far as to blur the line between reality and fabrication, posing unprecedented challenges for courts in assessing the authenticity and reliability of audio-visual evidence. Case Studies The most obvious ways deep fakes can be used to help circumvent the law are: The fabrication of evidence to provide alibis for activities and prove innocence. The fabrication of evidence showing someone’s guilt and involvement in a crime. Using the possibility of deepfakes to protest the legitimacy of authentic yet incriminating material7. The fabrication of evidence to provide alibis for activities and prove innocence. The fabrication of evidence showing someone’s guilt and involvement in a crime. Using the possibility of deepfakes to protest the legitimacy of authentic yet incriminating material7. Our first case study looks at fabricating evidence to incriminate someone wrongfully. In 2019, A deepfaked recording was used in a UK custody battle to discredit the father’s worthiness of shared custody. According to the father’s lawyer, Byron James, a “heavily doctored recording” had been presented to the court in which the father is heard making “direct and violent” threats to his wife[8]. However, after further examination, it was found that the recording presented in court had been manipulated to include words that had not been used by the father. In fact, demonstrative of how increasingly accessible editing technologies have become, the mother had used “software and online tutorials to put together a plausible file”[8]. While James had not previously encountered AI-doctored evidence in the courtroom, he has since commented on how it calls into question what kind of evidence can actually be trusted these days. Had they not discovered the tampering, and managed to obtain the original file and metadata, the mother may have successfully persuaded the courtroom as to the father’s fictitiously violent character. While the outcome of, and those involved in the hearing are confidential, it highlights the dangers of taking audio/visual evidence at face value. Not only this, but with the increasingly widespread ease and access to deepfake technologies, versus the relatively older age of judges, the necessary weariness of deep learning technologies may not be fully understood. Our second case study looks at people claiming real videos are deepfakes. In a more high-profile case, Elon Musk’s lawyers attempted to get a lawsuit against Tesla dismissed by claiming Musk had been the victim of deepfake videos. In 2016, Musk was videoed at a tech conference speaking about the extreme safety of Tesla’s Model S and Model X self-driving autonomous features. The videos from this interview have been on YouTube for 7 years now. However, in 2023, the videos resurfaced when a man died after his Tesla crashed while in self-driving mode, and the man’s family and lawyers cited those 2016 claims. Musk’s lawyers tried to deny their authenticity[7]. Being such a public figure, Musk is indeed the subject of deepfakes, which is what his lawyers tried to argue in this instance. The video here was, of course, real though, and the courts did not buy Musk’s lawyers’ claims. However, this highlights how in the age of deepfakes people can not only fabricate but also deny reality. “As people become more aware of how easy it is to fake audio and video, bad actors can weaponize that scepticism”[7]. Especially for public figures, deepfakes offer a layer of protection and immunity behind which they can hide, and avoid taking ownership of reality. Even for non-public figures, instances like this have been seen in courts before, and continue to increase in commonality. Deepfakes and International Regulation Recent regulations targeting deepfakes have emerged globally, with notable initiatives like California's disclosure requirement for AI-generated political ads[9] and the UK's Online Safety Act[10] mandating social media platforms to crack down on harmful content, explicitly including deepfakes. This said, the majority of deepfake regulation currently only really targets deepfakes used with pornographic intent, rather than to circumvent justice systems. While these regulations offer frameworks for accountability and deterrence, their effectiveness hinges on enforcement, resource allocation, skill, and adaptability. Furthermore, although regulation will only grow in necessity for deepfakes under many applications, governments and policymakers will have to consider the potential for negative unintended consequences. This includes impacts on freedom of speech and expression. Vaguely worded laws could lead to censorship or self-censorship, reducing participation in public dialogue and harming democracy. Balancing the objective of combatting deceptive deepfake media with safeguarding human rights will continue as a nuanced challenge, showing the complexities of the ethical considerations surrounding deepfakes, society, and public trust. References 1 What is deepfake AI? A definition from TechTarget What is deepfake AI? A definition from TechTarget What is deepfake AI? A definition from TechTarget 2 Exposure to untrustworthy websites in the 2016 U.S. election - PMC Exposure to untrustworthy websites in the 2016 U.S. election - PMC Exposure to untrustworthy websites in the 2016 U.S. election - PMC 3 AI deepfakes are poised to enter court proceedings at time of low trust in legal system AI deepfakes are poised to enter court proceedings at time of low trust in legal system AI deepfakes are poised to enter court proceedings at time of low trust in legal system 4 Authentication of Digital Audio Recordings Authentication of Digital Audio Recordings Authentication of Digital Audio Recordings 5 Understanding Digital Forensics: Process, Techniques, and Tools Understanding Digital Forensics: Process, Techniques, and Tools Understanding Digital Forensics: Process, Techniques, and Tools 6 Digital Forensics and the Chain of Custody: How Is Electronic Evidence Collected and Safeguarded? Digital Forensics and the Chain of Custody: How Is Electronic Evidence Collected and Safeguarded? Digital Forensics and the Chain of Custody: How Is Electronic Evidence Collected and Safeguarded? 7 People are trying to claim real videos are deepfakes. The courts are not amused People are trying to claim real videos are deepfakes. The courts are not amused People are trying to claim real videos are deepfakes. The courts are not amused 8 'Deepfake' audio evidence used in UK court to discredit Dubai dad 'Deepfake' audio evidence used in UK court to discredit Dubai dad 9 California Looks to Boost Deepfake Protections Before Elections California Looks to Boost Deepfake Protections Before Elections California Looks to Boost Deepfake Protections Before Elections 10 New laws to better protect victims from abuse of intimate images - GOV.UK New laws to better protect victims from abuse of intimate images - GOV.UK New laws to better protect victims from abuse of intimate images - GOV.UK

AI and the Law: Deepfakes, Eroding Trust, and the Legal Tightrope

Phishing is perhaps the most persistent cybersecurity threat, and it’s only getting worse. As long as people are people, they’ll make mistakes, so targeting human weaknesses is a surefire strategy for any cybercriminal. While that hasn’t changed over the years, new technology has led to a far more sinister version of these threats — deepfake phishing. What Is Deepfake Phishing? Deepfakes use deep learning algorithms to generate fake content that looks remarkably like the real thing. Falsified videos of a public figure doing or saying something they never said or did are common examples. Deepfake phishing uses this AI-generated content to craft more believable phishing attempts. In one instance, the CEO of an energy enterprise after getting a call from the parent company’s leader requesting the exchange. However, the real boss never ordered this transfer, and the “supplier” was a cybercriminal’s account. It turns out the call was a deepfake of the real leader’s voice. sent €220,000 to a supplier These attacks are dangerous because deepfakes are difficult to distinguish from reality. They may also lack telltale signs of phishing — like spelling errors or suspicious links — because they come in the form of audio or video content. As AI improves, they’ll only become more convincing, too. The Rise of Deepfake Phishing Deepfake phishing isn’t just dangerous — it’s growing at an alarming rate. According to one report, in 2023. The same research also found cybercriminals are using deepfake videos and pictures to get past biometric security, which can lead to account compromise attacks. deepfake fraud attempts rose by 3,000% This spike in deepfake phishing likely stems from deep learning models becoming more accessible. These advanced AI algorithms used to require high-level data science experience or a large budget. Now, plenty of free or low-cost advanced AI models are available that require little to no coding knowledge to use. This same trend will likely increase deepfake phishing attempts in the future. Generative AI is more accessible than ever. While this has many positive implications, it also means it’s becoming increasingly easy for cybercriminals to craft convincing AI-generated fake content. Email phishing costs U.S. businesses , even without prevalent deepfake threats. It’s only a matter of time before more criminals apply deepfakes to this lucrative and relatively easy attack vector. an average of $4.91 million How to Protect Against Deepfake Phishing While deepfake phishing is threatening, organizations can protect against it. The following steps will help businesses and their employees stay safe from these growing threats. Secure Account Access First, enterprises need to secure access to all internal accounts. Deepfake phishing is most effective when it comes from a real, trusted, but compromised account. Consequently, making it harder to break into them will undermine these attacks. Multi-factor authentication (MFA) is essential — and the kind of MFA people use matters, too. Security experts have warned deepfakes tools, so biometrics isn’t the most secure option. App-based one-time codes and hardware keys are safer MFA options in light of deepfake threats. can fool facial and voice recognition Train Employees As with regular phishing, employee training is also crucial. While deepfakes can be hard to spot, there are some common tells. Visual errors like juddering or blurring are common with deepfakes, as are strange audio cues like distorted voices. Teaching employees these signs and warning them of deepfake phishing will make them less likely to fall for it. Even with this training, people won’t be able to spot deepfakes with 100% accuracy. Consequently, it’s also important to stress that everyone should err on the side of caution. As a rule of thumb, if something seems suspicious or even just a little off, verify it before trusting it. Fight AI with AI As deepfake phishing becomes more popular, fighting fire with fire — or AI with AI, more accurately — will become more important. The same technology that makes deepfakes possible can help spot them, and several detection models are already available. Alternatively, businesses with extensive AI experience could build their own. Detection models can outperform humans in spotting deepfakes, but yet. Cybercriminals will develop new ways to get around them as methods improve, too. Consequently, while AI protections are helpful, organizations shouldn’t rely on them. no detectors are 100% accurate Impose Failsafes Because no human or AI model can spot deepfakes all the time, businesses need second and third lines of defense. Most importantly, workflow or technical stops should prevent one mistake from jeopardizing the organization’s security. Requiring multiple authorization steps for large financial transfers is a great example. Granting someone access to sensitive data or sending a large enough amount of money should require multiple people or authentication measures. These stops may hinder efficiency, but they give employees time to think about their actions. Involving more people and systems will also improve the company’s chances of spotting a deepfake attack. Monitor Evolving Threats Finally, brands and security experts should stay on top of deepfake and cybercrime trends. Cybercrime evolves constantly, and AI advances faster than many other technologies, so it’s easy for some defenses or best security practices to become outdated. New generative AI tools and some of these updates may make threats more dangerous. Alternatively, some updates may provide a more effective means of stopping deepfake phishing. Both changes are equally important to recognize. Frequent reviews of current trends and audits of internal security processes will help businesses stay on top of these shifts. , emerge several times a month Cybersecurity Practices Must Evolve Amid New Threats Phishing may not be anything new, but new technologies like deepfakes add a dangerous dimension. As exciting as AI is, it’s also important to recognize how cybercriminals can take advantage of the same tools corporations do. Failing to evolve cybersecurity practices alongside new technology could leave organizations vulnerable before they realize it. Awareness and training remain essential steps in preventing phishing attacks. When employees know they should look out for deepfakes, they’ll be less likely to fall for them. Emphasizing this human element while frequently adjusting technical defenses will keep companies safe despite these advancing threats.

Deepfake Phishing Grew by 3,000% in 2023 — And It's Just Beginning

ডিপফেক ফিশিং 2023 সালে 3,000% বৃদ্ধি পেয়েছে - এবং এটি সবে শুরু হয়েছে

Deepfake-Phishing ist im Jahr 2023 um 3.000 % gewachsen – und es fängt gerade erst an

El phishing deepfake creció un 3000 % en 2023, y apenas está comenzando

Le deepfake phishing a augmenté de 3 000 % en 2023 — et ce n'est que le début

2023 में डीपफेक फ़िशिंग में 3,000% की वृद्धि हुई - और यह अभी शुरुआत है

ディープフェイク フィッシングは 2023 年に 3,000% 増加 – そしてそれはまだ始まったばかり

딥페이크 피싱은 2023년에 3,000% 증가했습니다 — 이제 시작에 불과합니다

Deepfake Phishing cresceu 3.000% em 2023 – e está apenas começando

В 2023 году объем дипфейкового фишинга вырос на 3000% — и это только начинается

Deepfake Kimlik Avı 2023'te %3.000 Arttı - Ve Daha Yeni Başlıyor

Lừa đảo Deepfake đã tăng 3.000% vào năm 2023 - Và nó chỉ mới bắt đầu

2023 年 Deepfake 网络钓鱼增长了 3,000%——而且这才刚刚开始

So, I saw this article about deepfakes causing a stir, and let’s just say, the stakes are spicier than a mutton bunnychow on a Durban summer day. Here’s the pressing question: are deepfakes really the boogeyman everyone’s making them out to be? Don’t get me wrong, the potential for misuse is undeniable. Okay, let’s just take a minute to imagine; malicious actors manipulating political campaigns, spreading misinformation, or even worse, impersonating loved ones for nefarious purposes. Yeah! Scary stuff. Netizens have been talking about deepfakes for years, the forerunner of the deepfake has existed since 1997. However, last year there was an explosion in the popularity of deepfakes due to the boom in interest in artificial intelligence. 2024, & Big Tech is insane right now Google Gemini + Apple Vision Pro + Open AI’s Sora, talk about setting trends: a new era of technological alchemy, imagine the possibilities, reshaping perception and engagement… …is the Creative industry ready to navigate through this wizardry… But! there’s a flip side: deepfakes are built on powerful , and like any tool, it can be used for good or evil. We need to keep talking about how synthetic media can revolutionize education, healthcare, and even entertainment. I wish immersive learning experiences were around during my time, personalized medical simulations, or even hyper-realistic movies that blur the line between fiction and reality. AI technology Of course, with great power comes great responsibility. Yes, we need ethical frameworks, transparency, and user education to ensure deepfakes are used for good. But, why always fear the future, can’t we embrace the potential: I would have loved being in a classroom where you could feel the weight of Martin Luther King’s words through a speech while he walked through our aisle. Just think, after exploring the Amazon rainforest led by a virtual David Attenborough, or dissecting a virtual frog with a holographic Marie Curie guiding their scalpel (we all can relate to this life-defining moment when we decide to become a doctor or “pass out“) but can you picture your kids forgetting that kind of learning experience. They would be talking about it for days. Education : deepfakes could even be powerful empathy machines. The doctor could use a patient’s avatar to explain a complex diagnosis in a lot easier way to understand. Or, therapists can connect with those patients who otherwise hesitate to ask for help. I know I would appreciate the gap being bridged between medical jargon and my real-life experiences. Healthcare: Sign language interpreters could seamlessly appear in the news, Edu-learning materials can be translated into local dialects, and inclusivity for people with disabilities ~ hyper-realistic prosthetics that don’t just look real but react and move naturally. A world where anyone can connect and understand each other, regardless of background or ability. Accessibility: I remember when the Tupac hologram was used at Coachella, for those few moments, Tupac wasn’t just a memory on a screen. And yet, despite the initial shock, while some might argue it blurred the lines of reality, others saw it as a groundbreaking moment in entertainment. Entertainment: Deepfakes are already being used in these ways, and the potential is endless. The key? Responsible use. So, what I’m trying to say is, instead of panicking and throwing the AI baby out with the bathwater, let’s just take a breath and continue focusing on building safeguards. We robust detection methods, stricter regulations, and a culture of digital literacy that empowers everyone to be discerning consumers of information. Think of it like learning to spot a Photoshop fake in the age of social media. “deep” need Just my two cents: : Let’s be upfront about when deepfakes are used, especially for creative purposes. No more hidden agendas or blurred lines. Transparency is ALWAYs key : Equip people with the skills to critically evaluate online content. Media literacy needs to be a core education pillar. Empower the users : Work with tech companies, researchers, and policymakers to develop effective detection and prevention methods, not just knee-jerk regulations that stifle innovation. Collaborate, don’t regulate We all need to remember, that just like the internet was, now deepfakes are just tools. It’s still up to us to ensure it’s used responsibly and ethically. So, embrace the potential for good while mitigating the risks. And hey, if anyone needs help navigating this wild world of emerging tech, hit me up! I’m always down for a chat ( ). and maybe even a braai, if you’re in Durban Peace out, P.S. Don’t forget to fact-check everything, even this article!

Deepfakes — A Looming Threat or Misplaced Fear?

Remember that hilarious Donald Trump deepfake circulating online? While the technology behind it sparked amusement, its potential for harm lurks just beneath the surface. In Hong Kong, the amusement faded quickly when scammers used deepfakes to orchestrate a million-dollar heist, leaving a multinational company reeling and raising serious questions about the future of trust in the digital age. The human eye, once considered a reliable judge of reality, is facing a formidable challenge: deepfakes. But what exactly is a deepfake? In this article, we will dive into the realm of deepfakes where reality is not what it seems. Buckle up for a journey of learning about deepfake technology and how it was used to pull off a fraud costing hundreds of millions. Let’s Begin! Understanding Deepfakes Deepfakes are a type of artificial intelligence used to create fake images, audio, and videos that look convincing. Essentially, the technology is used to transform or replace an existing source’s content. For instance, deepfake can take a picture or a video and swap the person in it for another. Additionally, the technology has the ability to create completely original content where someone can be represented doing or saying something they never did. You must be wondering how this is possible. Well, deepfakes are created using an AI technology called deep learning. To put it simply, deep learning is a type of machine learning that allows computers to learn and recognize complex patterns. Upon analyzing the videos, images, and audio, the computer learns their patterns and is able to mimic them. The more data a computer has to analyze and learn, the more perfectly it will be able to mimic it. Impact of Generative Adversarial Networks on Deepfakes As if deep learning wasn’t enough, in 2014, we saw the emergence of GANs that completely revolutionized deepfakes. Can you imagine a technology that has two components: one generates a fake image, and the other judges whether the same generated image is real or fake? The generator learns to make deepfakes more realistic, and the discriminator learns to spot fakes better. This powerful and potentially dangerous tool is not only able to create deepfakes faster but also makes them more convincing. Now, you understand the kind of danger we are dealing with when it comes to deepfakes. The Hong Kong Scam When a child creates a deepfake video of their grandma rapping their favorite songs, the entire family sees the amusement in it. We also tend to ignore the obvious dangers that lie behind it. The situation gets a lot more serious when a targeted deepfake fraud results in a loss of millions of dollars. That’s exactly what happened recently in Hong Kong. The Setup What began as a routine work video call for a multinational company employee ended up being one of the biggest cyber crimes of the recent past. Let's find out. So, how was it all carried out using deepfakes? The scammers began with a text message conversation with the targeted employee. The individual message claimed he was the chief financial officer of the United Kingdom branch of the same company. The victim was asked to participate in an official work-related meeting called the that would involve four to six others. “Encrypted Trading Meeting” Considering the fact that the message and the invitation to an “encrypted” meeting seemed professional, there was no real reason for the victim to be suspicious of the entire situation. Another reason why this particular scam attempt was not detected was due to the involvement of multiple individuals. According to the acting senior superintendent of the Cyber Security and Technology Crime Bureau, Baron Chan Shun-ching, scams carried out in the recent past had all been through one-on-one video conferences. Hence, a video call meeting with multiple individuals was unexpected and, therefore, unlikely to be deemed a scam. Here, we can see the lengths to which the scammers were ready to go to carry out this sophisticated cyber attack. Designing and Executing the Deepfakes Four to six individuals were supposed to be involved in the video meeting. Creating believable deepfakes of a prolonged meeting, including the exact voices and body language, must have been a difficult task. Yet, the scammers were not only able to achieve this, but they did it successfully. Deepfakes begin with data in the form of pictures, videos, and audio. The attackers were able to source that information on the internet. Any videos and audio of the impersonation targets publicly found on the internet were downloaded and put through the rigorous process of deep learning. After thorough sophistication through trial and error, the scammers were able to successfully mimic both the voices and the facial features of the people involved. Once that was done, the scammers pre-recorded the deepfake videos so they did not have to interact with the victim. Posing as senior executives, the scammers intimidated the victim so he would not interrupt them in their master plan. Additionally, the scammers did not leave any areas uncovered. They even made sure to create fake personas resembling the staff from the UK headquarters’ financial department. The Outcome When the victim arrived at the encrypted video meeting organized by the financial director, he was confronted with pre-recorded deepfake videos of several people. The victim was only asked to provide a brief introduction of himself and was not given any other chance to interact throughout the call. During the meeting, the chief financial officer gave extensive instructions to the victim regarding investment. Finally, the moment came when the CFO ordered the victim to transfer funds to bank accounts. The victim had no reason to suspect anything, as all the individuals spoke convincingly and looked exactly as they were supposed to. Once the victim had been convinced to go ahead with the transaction of the asked amount, the conference call ended abruptly. The poor victim had no idea he was about to commit a blunder, costing his employers hundreds of millions. In the next week, the victim went on to make 15 transactions to five different bank accounts as instructed by the CFO. Once a total of was transferred, the victim contacted the UK headquarters only to find out all this had been a hoax, a fraud. HK$200 million This was one of many cyber crimes carried out in Hong Kong in the past year, with the total number of losses amounting to . HK$3 billion Detecting Deepfakes: Strategies and Preventive Measures If law enforcement agencies had a nickel for every time they encountered fake evidence, they could probably fund their own truth-detection agency. Hence, they are in a good position to tackle deepfakes and similar threats. However, the ever-evolving and constant advancement of AI technologies has made deepfake detection more challenging than ever before. Keeping that in mind, here are some of the best ways to detect synthetic content and the preventive measures we can take against a threat of that kind. Manual Detection Believe it or not, no matter how sophisticated deepfakes have become, it is still possible to detect them with the naked eye if you know what to look out for. A deepfake image, video, or audio will inevitably have some sort of inconsistency; that is what you should aim to detect. However, this can only be done for a limited number of files. You won’t go looking at a thousand different files. This technique also requires proper training to master. To begin with, there are certain key inspections you can detect upon a close examination of an image. For instance: Blurring around the edges of the face in a picture or a video No or very little blinking from the individual in the video Light reflections in the eyes Inconsistencies in the person’s hair or vein patterns and scars on the face or body Background inconsistencies However, this process is further complicated by your perception of a video or audio; whether you choose to look for these inconsistencies on a file is entirely up to you, and thus errors may occur. Automated Detection To avoid the imminent risk of human error, there has to be a system that can automatically detect deepfakes for any files. However, such a system is unlikely to be 100% accurate. Nevertheless, with the constant improvements in advanced technologies, a system can be constructed that offers increasingly accurate results. Some examples of detection tools are as follows: Looking for imperfections and natural changes in skin color or facial features. Biological Signals: Searching for inconsistencies in dynamics of the mouth, viseme. Phoneme-viseme mismatches: Seeking imperfections in facial and head movements Facial Movements: Key Takeaways Considering the emerging trends in cybersecurity, deepfakes are a fake reality that poses a great threat to our personal, social, and financial lives. Not knowing whether a video, picture, or audio is real or fake can have catastrophic consequences, as seen in the case of the Hong Kong scam. However, with the correct detection techniques, both individuals and multinational companies can detect the authenticity of a file with considerable accuracy.

Deceptive Doppelgangers: How Deepfakes Caused a Scam of HK $200 Million

Stories, News & Mentions about DEEPFLARE

Deepflare’s Stories on HackerNoon