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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?

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

The Noonification: Not All Deepfake Detectors Are Created Equal (12/10/2023)

Deepfakes have been on the rise for the last few years, with multiple face swap tools gaining popularity among fraudsters and even organized criminal groups. According to the Europol report “ ,” deepfakes could even be used in more serious crimes such as CEO fraud, evidence tampering, and the production of non-consensual pornography. Facing Reality? Law enforcement and the challenge of deepfakes However, as with everything related to AI, it’s always an arms race between fraudsters and modern deepfake detectors. Coming out of International Fraud Awareness Week, we wanted to provide a reality check into the capabilities and advancements of deepfake detectors over the last few years - a reality check needed only because of how vast an issue deepfake fraud remains. In our internal research, we analyzed the performance of open-source modern state-of-the-art deepfake detectors published since 2020. Here’s our fundamental observation: when it comes to distinguishing between real and fake content, computers have long outperformed humans. This finding underscores the need to harness the power of cutting-edge algorithms and methods. Almost all of the leading works in this field prominently feature face detection as a fundamental element of their algorithms. Face detection is a near-solution, characterized by high accuracy—not perfect, but close. When a face is prominently positioned in an image and looks forward, modern detection models excel at fast and reliable identification. And while there are several ways to create deepfake images, one method stands out as both popular and robust: one-shot face swapping. This technique uses two images, a source and a target, to transfer facial features from the former to the latter. In the current landscape, it’s considered the most powerful approach to creating deepfake images and videos. You can try our yourself, and check it out. Deepfake game Studies The lack of readily available code and weights in the majority of related work underscores a common challenge in the field of deepfake detection. This landscape often prioritizes business applications over scientific dissemination, resulting in limited access to the tools and resources that are essential for academic and research communities. This lack of openly shared code and model weights has been a significant barrier to the broader advancement of deepfake detection methods. There are numerous approaches to deepfake detection, and with each conference, new articles appear. Some of these articles focus primarily on the model architecture for deepfake detection, drawing considerable inspiration from the transformer model and attempting to adapt it to the challenge. Meanwhile, other articles focus on training methods, particularly on synthetic datasets filled with fake images. The field is rich with benchmarks, and in the following section, we will discuss some of the most powerful among them, emphasizing those with open-source code and weights available. FaceForensics++ The most prominent baseline for all modern deepfake detection methods is the research published in the paper . The authors' primary contribution is an extensive dataset of over 1.8 million images from 1000 YouTube videos, categorized into raw, high-quality, and low-quality options. FaceForensics++: Learning to Detect Manipulated Facial Images They used human observers to validate these distinctions. The deepfake classification model in the paper is a binary system based on an XceptionNet backbone with ImageNet weights, fine-tuned on their dataset. By employing a simple voting mechanism based on model responses, the authors achieved a significant impact in the field of deepfake detection despite the model's architectural simplicity. Multi-attention Deepfake Detection The authors highlight a common problem in previous deepfake detection models which are primarily characterized by their reliance on a simple binary classifier approach. The basic binary classifier approach that doesn't account for subtle distinctions between real and fake images. The authors here propose an alternative inspired by fine-grained classification, using a multi-attention network with multiple attention heads to focus on different artifact regions. This network combines low-level texture features and high-level semantic features to create image representations and a distinctive attention-guided data augmentation mechanism for training. This approach addresses the limitations of existing models, making it a promising method for deepfake detection. Multi-modal Multi-scale Transformers for Deepfake Detection The authors of "M2TR: " emphasize the importance of focusing on specific regions in images where fake content may appear. Multi-modal Multi-scale Transformers for Deepfake Detection They introduce a multi-modal approach with a multi-scale structure, using a frequency filter to detect artifacts that may not be visible after compression. They further employ a Cross-Modality Fusion block inspired by self-attention to merge RGB and frequency features into a unified representation, enhancing their deepfake detection method. End-to-End Reconstruction-Classification Learning for Face Forgery Detection In " ," the authors address a common issue in deepfake detection methods that focus on specific forgery patterns, which may not encompass all possible manipulations. End-to-End Reconstruction-Classification Learning for Face Forgery Detection They propose an approach based on two components: reconstruction learning and classification learning: Reconstruction learning enhances the representations to detect unknown forgery patterns. Classification learning identifies disparities between real and fake images. The authors employ a multi-scale approach to improve these representations, using a dedicated reconstruction network to model real faces and a metric-learning loss to enhance the detection of previously unknown forgery patterns. Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization In the work, " ," the authors address a significant issue concerning deepfake detection. They point out that many deepfake models are based on face-swapping techniques, which can lead to a unique challenge. Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization These models tend to remember the distributions of genuine IDs, which means that a fake image can sometimes appear as a blend of two different IDs. However, this problem becomes especially challenging when attempting to apply these models to new, unseen, or cross datasets. In these cases, the model struggles to decipher the true identity of the image because it has not encountered it before. To address this issue, which the authors refer to as "Implicit Identity Leakage," they endeavor to find solutions that improve the generalization of deepfake detection models beyond the confines of their training datasets. To provide evidence of this phenomenon, the authors initially took pretrained deepfake classifiers and froze all layers except the last one. They replaced the last layer with a linear layer and fine-tuned it for an ID classification task. This experiment showed that a single linear layer could be effectively trained to classify IDs with high accuracy, demonstrating the potential for identity leakage. Then the authors created a new method for swapping parts of the face at different scales, with a primary focus on swapping specific facial regions. They then trained a multi-scale detection model by utilizing images generated from this process. This model scrutinizes feature maps of different sizes in diverse layers to detect the existence of artifact areas, delivering a thorough observation of the likely signals of deepfake manipulation. Detecting Deepfakes with Self-Blended Images The latest notable paper in the field of deepfake detection is " ." In this research, the authors have taken a novel approach by training their own model using a unique dataset. Detecting Deepfakes with Self-Blended Images This dataset consists of images generated through the blending of pseudo source and target images derived from individual pristine images. This process effectively replicates common forgery artifacts often encountered in deepfakes. The key insight behind this approach is that by using more general and less easily recognizable fake samples, classifiers can learn more generic and robust representations without succumbing to overfitting to manipulation-specific artifacts. The authors identify four primary types of common deepfake artifacts: landmark mismatch, blending boundary, color mismatch, and frequency inconsistency. They then synthesize these artifacts using a specialized model. For the model architecture, the authors took EfficientNet-b4, pre-trained on the ImageNet dataset. They fine-tune this model on their Self-Blended Images (SBI) dataset, ensuring that the model becomes adept at detecting deepfakes by learning from these blended images with common forgery artifacts. Our Experiments. Metrics and Datasets We have analyzed the performance of modern state-of-the-art deepfake detectors that were published after 2020 and have their code and model weights available for public and research use. We have calculated relevant metrics for each model on the same public datasets to see how qualities disclosed by authors are transferred to a similar dataset. Then we applied simple transformations that are frequently used by fraudsters to bypass verification (like face swap) and saw how efficiently deepfake detectors perform. We used and as base datasets for ground truth real images. Both are widely used in research and development. The images from these two datasets could be classified as “domain” images for most of the computer vision tasks. Celeba-HQ LFW To introduce a ground truth fake images datasets, we used a state-of-the-art . It is still considered by many as the best and most popular single-photo deepfake generator. deepfake model from 2021 called SimSwap To generate a sufficient amount of images, we used random pairs of source and reference photos from the dataset to create Fake-Celeba-HQ and Fake-LFW. Each dataset is exactly 10.000 images. For simplicity, the main metric for measuring the quality of the models we used 1-class accuracy with a default threshold of 0.5. In other words, for each dataset, we calculated the percentage of rightly guessed labels. Additionally, we calculate a total ROC-AUC metric over combined real and fake datasets. Experiment 1: LFW CelebaHQ Fake-LFW Fake-CelebaHQ AUC score SBI 0.82 0.57 0.82 0.96 0.84 CADDM 0.49 0.69 0.80 0.54 0.67 RECCE 0.01 0.00 0.98 0.00 0.54 MAT 0.00 0.74 1. 1. 0.75 FF++ 0.13 0.67 0.88 0.53 0.57 M2TR 0.42 0.56 0.69 0.51 0.56 Table 1. 1-class accuracy and AUC for real/fake datasets without changes As expected, most of the models had some issues detecting SimSwap deepfakes. The best model is SBI, scoring 82% and 96% showing a promising 0.84 AUC score. What is unexpected is that there are a lot of capable models that had difficulties classifying images from real datasets as real: RECCE scored most of the real images as fakes. MAT, FF, and M2TR scored less than half of the faces from LFW as deepfakes. There are 3 models that have an AUC score close to 0.5. This raises questions about the transferability of these models to a more realistic domain and how they can easily be bypassed by fraudsters. Experiment 2: To test how these models behave on a more realistic domain, we will try two different techniques fraudsters usually exploit when using deepfakes. The first thing they do to hide most of the artifacts and irregularities is downscaling. Since, in most of the liveness and deepfake checks there are no requirements on the video quality, fraudsters usually compress deepfaked video. To simulate this approach, we will use the same datasets, but compress each image to a much smaller resolution (128x128) using a bilinear algorithm. Ideally, deepfake detectors should be able to detect deepfakes even if the resolution of images on inference differs from the resolution during the training process. LFW CelebaHQ Fake-LFW Fake-CelebaHQ AUC score SBI 0.82 0.82 0.43 0.23 0.6 CADDM 0.55 0.46 0.62 0.65 0.6 RECCE 0.83 0.89 0.13 0.08 0.54 MAT c40 1. 1. 0. 0. 0.5 Figure 2: Best of the deepfake detectors’ metrics on a low-quality dataset Here, the results are more than confusing. Models that were achieving more or less competitive performance now have near-zero accuracy on fake datasets. One can see that the MAT model simply scored everything as a real image, and the RECCE model is very close to the same decision. Experiment 3: The second fraud practice is to upscale the image to retouch deepfaked images to remove all imperfections that could “give up” fabricated images to detectors. One of many such examples is eyes: there are no round pupils or light refractions on most deepfaked images. So, a fraudster usually uses some specific beautification or “enhancement” software similar to the ones used in Instagram or TikTok to mask all impurities. To simulate the effects of such software, we used its closely related . This is an enhancer that uses GANs to upscale the face and enhance it. open-source analog: GPEN LFW CelebaHQ Fake-LFW Fake-CelebaHQ AUC score SBI 0.76 0.63 0.38 0.58 0.62 CADDM 0.52 0.71 0.59 0.38 0.57 RECCE 0.18 0. 0.8 1. 0.52 MAT c40 0.99 1. 0. 0. 0.5 Figure 3: Best of the deepfake detectors’ metrics on an enhanced dataset Here, one can see the same trend as in Experiment 2. The MAT model scored everything as real and RECCE scored everything as fake. The performance of SBI and CADDM is better than random, but they missed more than half of the deepfakes in Fake-LFW and Fake-CELEBA-HQ datasets. Conclusion The outcome of this research is gloomy since there are no open-source deepfake detectors that would be 100% secure, while deepfake fraud is expected to develop further, as its generation is getting easier and cheaper. According to Sumsub’s internal statistics, the prevalence of deepfake fraud grew considerably from 2022 to Q1 2023: From 2022 to Q1 2023, the proportion of deepfakes among all fraud types increased by 4,500% in Canada, by 1,200% in the U.S., by 407% in Germany, and by 392% in the UK. In Q1 2023, the most deepfakes came from Great Britain and Spain with 11.8% and 11.2% of global deepfake fraud respectively, followed by Germany (6.7%), and the Netherlands (4.7%). The U.S. held 5th place, representing 4.3% of global deepfake fraud cases. Our experiments show that there are still a lot of things to be done about deepfake detection. Even the best open-source deepfake detection models are not prepared for the real world and can’t combat fraudsters. There are a great number of papers about deepfake detectors, but most of them don’t have code or model weights available. Because of that, one of the issues here is the lack of openness which creates a barrier to the improvement of deepfake detection methods. Therefore, we at Sumsub: Are continuously combatting deepfakes and improving our . in-house deepfake detection technology Offer our in-house set of four distinct machine learning-driven models for deepfake and synthetic fraud detection called . It is available for everyone for free download and use, and Sumsub seeks feedback from the AI-research community to further improve the models’ capabilities. For Fake’s Sake Still, the main responsibility for the online protection of Internet users’ images lies on the users themselves. Remember to be cautious about sharing personal photos online. Better use stylish avatars instead, just like our authors did. And you can find other useful tips on how to stay away from deepfake abuse. , here Written by Maksim Artemev, Lead Computer Vision Engineer, and Slava Pirogov, Computer Vision Engineer, at Sumsub

Not All Deepfake Detectors Are Created Equal

সমস্ত ডিপফেক ডিটেক্টর সমান তৈরি করা হয় না

Nicht alle Deepfake-Detektoren sind gleich

No todos los detectores de deepfake son iguales

Tous les détecteurs Deepfake ne sont pas créés égaux

सभी डीपफेक डिटेक्टर समान नहीं बनाए गए हैं

すべてのディープフェイク検出器が同じように作られているわけではない

모든 Deepfake 탐지기가 동일하게 생성되는 것은 아닙니다

Nem todos os detectores Deepfake são criados iguais

Не все детекторы Deepfake одинаковы

Tüm Deepfake Dedektörleri Eşit Oluşturulmamıştır

Không phải tất cả các máy dò Deepfake đều được tạo ra như nhau

并非所有 Deepfake 探测器都是一样的

In recent years, we witnessed a curious phenomenon—digital humans and deepfakes—that appeared at the intersection of artificial intelligence (AI) and media technology. China, known for its technological advances, was one of the first countries to adopt and nurture the development and deployment of these technologies. In this article, I will demonstrate the use of AI-driven digital humans and deepfakes in various industries and media channels. In the example of China, we can see what potential these technologies can bring and what ethical considerations they may raise. First of all, let’s find out what digital humans are. They are images and videos of digitally created people, so-called AI avatars, that are able to communicate with users and viewers in a very human-like way—in real-time. They are created using a number of the latest AI technologies, such as computer vision, machine learning, and natural language processing. Computer vision is used to capture images and videos of real people on which the AI avatars will be based. Machine learning algorithms train a model to generate realistic images and videos of the person. For some algorithms, only a few photos or a short video of a real person is enough to create a convincing AI clone—a digital persona that would move and interact in a realistic way. Natural language processing algorithms and speech-generating tools enable the digital avatars to communicate like real people. Combined, these tools produce an exceptionally realistic picture that often can be mistaken for a real person. AI Avatar Generators Answering the growing demand for AI avatars, Chinese companies produce more and more affordable AI generative tools that can be used for digitizing real humans as well as creating unique digital personas. Meishe, a solution provider that focuses on independent development of video and audio SDK for mobile devices, has recently revealed three AI avatar generators: they allow creating an avatar using just a photo, video, or 3D model (GLB file) in a few minutes time—for a very reasonable price. This makes the technology more accessible both for businesses and individuals. Concurrently, , a leading Chinese tech company, is starting the internal testing of its platform for interaction with digital personas. Their solution is based on an advanced language model that allows cross-format generation. Qihoo 360 Unlike Meishe, which bases its AI avatars on real humans, Quihoo 360 focuses on creating a wide range of personalities such as digital employees for marketing, operations, and programming purposes, as well as an interactive platform that includes classic Chinese literary figures. Deepfake Technology Another Chinese Tech giant, Tencent, put its effort into democratizing deepfake technologies. The company recently presented a platform that allows the creation of a deepfake digital human for an astonishingly . affordable price of $145 This new service allows anyone to make a high-definition digital human based on just three minutes of live-action video of a real person and 100 sentences of voice material. Digital Humans in Business In China, companies have already started to employ virtual humans for various tasks. “Hiring” a virtual person would cost approximately $14,000 a year, which can be considered pretty cost-efficient. AI-powered digital humans can help streamline operations and also open up new possibilities for engaging with customers and stakeholders 24/7. Digitized humans can answer questions, interact on a live stream, and even sing. This means that their use is not limited to just chatbots, automated technical support, and customer support—even though it’s becoming very common in many business areas such as financial services and tourism. AI avatars can also act as social media influencers and even become broadcasters on the state media. According to , the use of virtual humans in various business areas is expected to grow by 50% annually through 2025—thanks to the rapid development of the technology, its increasing availability, and new applications in various fields. CNBC AI News Anchors Recently, Chinese media has introduced who would engage with viewers and answer questions during important social events, particularly, the “Two Sessions”—the Chinese government's annual plenary sessions held every March in Beijing. AI news anchors These digital news anchors are able to provide real-time responses and engage with the audience. It is evident that the Chinese government views AI avatars as a significant asset that can help advance and propagate Chinese culture and values, investing in the development of the technology. Influencer Live Streaming Deepfakes, which are the AI avatars of real people, have become a significant part of Chinese e-commerce, particularly among livestream influencers. Live streaming has transformed into a billion-dollar industry in just a few years and is expected to grow by at least 20% a year in the future. Deepfakes take on the appearances and voices of and seamlessly integrate into livestreams, taking content creation to a whole new level. popular influencers The integration of deepfake technology into live streams is a powerful tool that may redefine influencer marketing for e-commerce purposes. Chinese companies are actively integrating this technology to boost sales and brand reach, and it proved to be highly beneficial for the overall viewing experience and elevates the revenue from livestream shopping. However, this advancement also raises ethical concerns. Many people see the need for regulations that would ensure transparency and protect consumers from fraud and misinformation. AI Cloning To create highly interactive and personalized AI-driven avatars, some Chinese tech companies have introduced AI cloning programs. The idea is to use volunteers and testers who will participate in recording their voices and appearances for the creation of more advanced and realistic AI personas based on that data. For example, Microsoft spinoff Xiaoice in China and Japan, seeking testers who can “lend” the AI avatars their personas, voices, and appearances in order to further refine and develop virtual beings. If the first testing group appears successful, the company is planning to expand the number of AI avatars to a hundred thousand. has launched an AI clone program Other China's tech giant, SenseTime, based on this method. In addition to a chatbot, the company showcased a digital avatar creation platform and a pair of complementary 3D modeling tools, showcasing the integration of AI-driven virtual assistants into various applications and industries. has unveiled new AI products These initiatives can help integrate highly personalized AI clones and digital humans into our daily lives, however, this approach poses a risk of unauthorized data collection. Many AI tools over the world have already been accused of collecting users’ data without consent. With the growing amount of video and audio content on the web, chances are high that anyone’s social media posts can become training material for an AI cloning tool. Market Perspectives China is one of the world leaders in AI development, amassing 19% of the total AI funding deals globally. It is also one of the major producers and consumers of the digital avatar industry. According to , the global digital human avatar market is expected to reach USD 527.58 billion by 2030, with the Asia-Pacific region accounting for the largest share. a report by Emergen Research The key factors that facilitate explosive growth of the AI avatar market in China are the following: Social media propelled demand for virtual content and surging metaverse trends. AI avatars help users engage in interactive virtual experiences and allow the creation of high-quality content 24/7. Dropping costs of developing and managing AI avatars make the technology largely accessible for Chinese businesses and individuals, helping it gain a broader audience. Government support and encouragement play an indispensable role in the country’s economy, amplifying the potential of Chinese AI solutions on the global market.

The Rise of Digital Humans and Deepfakes in China

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