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Below are some of the most notable products they have on display: The Walkman revolutionized listening to music on the go in the 1980s. And in 1983, Audio Technica debuted the AT-727 Sound Burger, a portable turntable.

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Music: "Prelude Carmen" by Jerusalem Symphony Orchestra via Artlist; "Life's Journey Begins" by idokay via Artlist; "Thoughts" by ANBR via Artlist; "Cherubinos Aria Voi che sapete" by Jerusalem Symphony Orchestra via Artlist; "Little Things" by ANBR via ...

TEL AVIV, Israel, Sept. 12, 2023 /PRNewswire/ -- Artlist, the creative technology company used by millions of digital creators and major brands worldwide, has launched the 'Artlist 100K Fund ...

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One larger-than-life idea TEL AVIV, Israel, Sept. 12, 2023 /PRNewswire/ -- Artlist, the creative technology company used by millions of digital creators and

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One creator | $100,000 USD | One larger-than-life idea TEL AVIV, Israel, Sept. 12, 2023 /PRNewswire/ -- Artlist, the creative technology company used by millions of digital creators and major brands worldwide, has launched the 'Artlist 100K Fund' -- a ...

Artlist, the creative technology company used by millions of digital creators and major brands worldwide, has launched the 'Artlist 100K

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AI is growing faster than ever. MarketsandMarkets that the artificial intelligence market will skyrocket to $407 billion by 2027. In this constantly changing environment, you either adapt or risk falling behind. As a result, companies and businesses around the world are incorporating AI into their day-to-day operations. According to a conducted by Forbes Advisor, a whopping 64% of businesses are confident that artificial intelligence will boost their overall productivity. A very optimistic outlook, isn’t it? predicts survey Today, it’s hard to imagine any aspect of our lives without AI, and influencer marketing is no exception. According to a by the micro-influencer platform SocialPubli, recent study 82% of marketers view AI as an opportunity for growth and success, with 76% already using AI in their work. Last year, Meta's of partnerships with "cultural icons and influencers" sparked many discussions. The company created AI-powered chatbots with unique interests and personalities and their own Instagram and Facebook profiles. Some resemble such celebrities as Snoop Dogg, Charli D’Amelio, Kendall Jenner, Tom Brady, Paris Hilton, and others. announcement While some see AI as a threat that could eventually take over human jobs, it's important to understand the many benefits that it can offer to marketers. Right now, However, the potential of AI certainly spreads far beyond these areas. AI is in influencer marketing to brainstorm ideas (45%), write social media posts (47%), create images and videos (39%), do editing (36%), and conduct research (35%). already being used How AI Can Revolutionize Influencer Marketing Discover relevant influencers with a relevant target audience When it comes to choosing influencers for collaborations, understanding their target audiences is key. While some platforms claim to be well-fitted for this task, manual review and verification seem to be more reliable at the moment. AI tools are already here to help, but in my experience, their current data doesn't quite match up with actual sales performance. They either miss important factors, or their methods are off. So, let's just imagine that AI keeps getting better and will one day reach a point of perfection. How could it help with searching for relevant influencers? When working with influencers, you should always watch out for certain red flags like low-quality followers, low frequency of posts, or low engagement rates. AI could help by performing initial checks on influencers and eliminating those who don't meet these standards. I can’t even begin to explain how much time it could save. Moreover, AI could check subscriber engagement, comment frequency, the sentiment of comments, any involvement in scandals, and alignment of political views with the brand's policy. In theory, interests could be analyzed as well. But automatic assessment of interests can be very complex because each audience category can have many subcategories. For example, imagine trying to sell a healthy vegan product. The system might suggest a list of influencers interested in healthy eating, but what if one of them supports healthy living but opposes veganism? You'd only catch these nuances through manual review. AI could also consolidate data from various platforms. Instead of manually copying and pasting links and calculating average metrics, AI could streamline this process by integrating all the data. It would also be a significant time-saver. Create and edit content According to the , 52% of video content creators are already incorporating AI into their workflow. However, I don't believe in AI replacing creative professionals anytime soon. I’d say that at this stage, it primarily produces content at the level of a junior manager. Artlist Trend Report 2024 Nevertheless, there are numerous areas where AI can be very beneficial. For example, many creators struggle with writer's block or feel lost when they need to come up with a cutting-edge and highly effective creative. AI can inspire and generate ideas as a starting point. AI is very accessible and very cost-effective. This makes content creation more budget-friendly, as even basic free versions of AI can offer some guidance. However, most AI platforms today have significant limitations and require validation. You need to check their outputs carefully because results can be very different — some are just funny, while others are alarming. Streamline your daily workflow Once again, AI is still in its early stages, so there are plenty of mistakes, and most of the data isn’t very accurate. While AI can provide guidance and explain some processes, you still need to proofread and verify everything carefully. But we're here to dream about the future. AI could help create spreadsheets, develop formulas, or even generate reports. Imagine you could simply say, "I need to design a spreadsheet for this advertising campaign. I'm stuck on the layout; can you help?" And it would create a template for you. Moreover, if you work with many managers or agencies, they are likely to send you information in various formats, and AI can help you standardize it. I once had an experience where AI proved incredibly effective for a similar task. I was collaborating with different agencies and needed to create a stop list of influencers to ensure they wouldn't be approached with conflicting proposals. There were two main requirements: the list had to be dynamic, and it had to be easy to input new influencers and check if they were already on the list. AI helped me develop a script for the spreadsheet. As a result, this tool has been refined and it’s being widely used today, saving a lot of time. Analyze Campaigns and Launches AI could monitor and analyze engagement, click-through, and conversion rates, as well as other metrics, providing insights for future strategies. Today, many businesses are flooded with data that includes sales figures, lead generation statistics, influencer performance, various marketing channels, methodologies, and timeframes. If we imagine that the future is here and ChatGPT-6 is already available, you could just ask, "Hey, I'd like to know the impact of these content creators on our overall business performance," or "I want to determine the most and the least effective creators." In general, every brand employs at least one analyst dedicated to tasks like these, spending days or even weeks writing scripts, utilizing SQL, and extracting data. But with AI, you could just write a query, and you're good to go. In my personal experience, AI helped me build regression analysis models to examine the correlation between sales and influencer marketing campaigns. It suggested certain tools, allowing me to discuss them with an analyst and implement them later on. Detect Fraud Currently, most fraud detection is handled manually by managers. For instance, if an influencer produces boring content but has a lot of likes, it can raise suspicion. Other red flags include frequent spelling mistakes, abnormal likes, or comments that don't match the post. Managers have to manually verify each account, checking for consistent content or a detailed profile description. Sometimes, it's easy to spot fake accounts, like those with a single profile picture, a mismatched description, or a random link. AI could automate this process using mathematical models. It would analyze subscription patterns, saving managers time and allowing them to focus on more important tasks. Increase outreach One of the most crucial parts of the influencer sales funnel is outreach. Your goal is to enhance the funnel's early stages with basic messages like, "Hi, I work with this amazing brand, and we have this fantastic product. We'd like to collaborate with you." Such tasks can be easily delegated to a bot, ideally programmed to simulate human interactions. After the initial contact, a manager can take over. Certainly, AI’s conversion rate may not match that of your top-performing managers. Moreover, since you're not making personalized approaches to content creators, there's also a risk of missing out. For instance, if you simply send a generic message to an influencer saying, "Hey, let's collaborate," they might ignore it. However, if you had taken the time to mention something specific about their content, it would have been more personalized, increasing the chance of a response. The risks of using AI Some experts argue that AI lacks authenticity and emotional connection. But I disagree, and I believe you can customize it to have a specific tone of voice and even add some humor. However, the ethical implications are significantly more important. Let's face it: not everyone enjoys talking with a bot, especially when it pretends to be human. The issue of ethics can arise when content creators are contacted by a bot and later feel deceived because they thought they were interacting with a real person. Moreover, as I’ve already mentioned many times, current AI platforms are not flawless, and depending on them for analytical tasks or calculations can be risky. To make things worse, some errors may not be immediately obvious. For instance, while mistakes in math formulas can be identified and corrected, it’s not that easy to see errors in influencer selection. You might think you have received a comprehensive list while actually missing out on some valuable influencers. Lastly, there are some concerns about data security. There's a risk that AI might start uploading data inputs from other users, possibly including your own. Avoid using confidential sales data, revenue figures, or other sensitive information. However, most influencer statistics, such as links, reach, likes, and comments, can generally be used without concern. The Recipe for Success How can everyone in influencer marketing use AI to boost their performance and make their work more effective right now instead of waiting for the perfect AI tools of the future? Instead of 'Google it,' try 'AI it': Use AI as your search engine, especially if you have an unresolved issue. AI can guide you to some unconventional solutions. Study all market cases: Understand AI's capabilities and gather case studies to anticipate future development. Technologies evolve at a rapid pace, and falling behind means having to play catch-up for a long time. Engage with AI when you're bored: Sometimes, interacting with AI and asking random questions can be quite intriguing. Like, what was the first-ever social media post? Exercise critical thinking: Since all AI platforms have their limitations, it's essential to validate their outputs. Stay curious and open-minded: While AI isn't an immediate game-changer or a genius, it's a valuable tool that can significantly simplify and speed up many tasks.

6 Game-Changing Ways AI Can Boost Your Influencer Marketing

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is none other than a series of low-cost highly effective marketing experimentations done repeatedly until you find the strategies that work. Growth Marketing Here is a list of that have worked for me that I invite you test out in 2023. 6 growth marketing strategies Growth Marketing Strategy #1 - YouTube Content Marketing As per Oberlo, , right after Google. YouTube is the world's second biggest search engine Most startups are generally intimidated when it comes to creating YouTube Content, primarily because they think they'll have to compete with creators along the lines of Mr. Beast. But that's not the case. As of now, the YouTube content delivery system consists of 3 algorithms: The Home Feed. The Shorts Feed. The Search Feed. One of the lowest-hanging fruits out there is getting your content to rank on both high & low intent keywords of the so-called search feed. Here's how I did it: I run a Growth Hacking Course ( ) and a Lead Generation Agency. Growth Hacking Bootcamp Throughout the past couple of years, since November 2017, YouTube has been an essential part of my client acquisition strategy. To this day, videos that I created years ago, still bring me an influx of leads, clients & community members. The only investment required from my side was some keyword research, and a few hours to record & edit the content. The strategy is simple: Identify high volume - low competition keywords using tools like & to understand what content is worth ranking for. VidIQ Google Trends Create videos around those keywords. Edit the videos. Create some appealing thumbnails using tools like Canva or Adobe Photoshop. Post your videos onto YouTube. Rinse & Repeat. Do this process enough times, and after several weeks to months, enjoy the benefit of YouTube traffic converting on your website. What's great about this, when compared to ad spend, is that YouTube traffic is continuous. Post something on LinkedIn & Twitter and it's off the feed within 48 hours. Positioning something on Youtube, however, lets you take advantage of what's called evergreen content - content that's positioned to never die out. Growth Marketing Strategy #2 - TikTok Content Marketing TikTok is among the most generous platforms out there when it comes to giving new creators a worthwhile number of views & overall engagement. Just about anyone can roll up on TikTok with a fresh account and become the next influencer if they dedicate the right amount of time & consistency necessary. But there's a catch. They need to understand & adhere to some best practices. This applies EVEN MORE to companies looking to generate both revenue & targeted traffic from TikTok. The issue with most companies & entrepreneurs getting on TikTok today is that THEY THINK they can just post content on anything and have it pop off. But that's not the case. Here's how you can do it right: Successful "TikTok-ers" follow several best practices when it comes to creating content that's positioned for at least some form of virality. Firstly, they understand the limitations of the TikTok algorithm. They know it's good, but they also know that it'll be unable to connect SUPER SPECIFIC content with a large audience. An example of content that TikTok can easily connect to an audience is content like: How to make money as a teenager. How to file your taxes. How to cook risotto. How to find a partner. The issue is though that most people attempting to become TikTok Content Marketers create content that's way too niched, giving the algorithm a very hard time when it comes down to pairing your content with an audience that may be interested in what you have to show. An example of hyper-niched content: How to generate leads for the accounting industry In this example, TikTok must not only find individuals that are interested in lead generation, but also people interested in generating them in the accounting industry. In addition to the above, most people also give up after pumping a few hyper-specific videos that: Are not subtitled. Have no hook within the first 3 seconds of the video to capture the attention of the viewer. Have no overlay content on top of the core video to make the video more appealing. Have no background music. Do not educate, amuse, or intrigue the audience. So the real question is, why should TikTok give you views, engagement & traffic when you can't even get the basics right? It shouldn't. It should instead prioritize the content creators that understand & follow the best practices of the platform. Growth Marketing Strategy #3 - Community Building Communities are some of the biggest Growth Marketing hacks any company can employ regardless of the year they're in. Communities can be built around entertainment or information and can be hosted on a variety of platforms. The thing with online communities though is that they require time & manpower. But once you get it right, you have a huge asset in your growth marketing arsenal. Here's how I did it: I built my own communities using both WhatsApp & Facebook Groups as hosting platforms. The process was simple. I identified where my target market lies and launched numerous outreaches across both LinkedIn & Reddit inviting fellow marketers & growth hackers into our growth hacking communities. The invitation was very simple, I simply invited them to a free community where they can network & learn FOR FREE from marketers & entrepreneurs from a wide array of industries. Many joined, and many didn't. Once I hit a good number of initial people in the community, I not only started engaging with them daily, but I also started creating relevant content for them related to the topic of the community (in this case Growth Hacking). This helped develop somewhat of a business flywheel, where I: Created a community. Created content for the community to help keep it engaged & lively. Added a join our community call to action on all the content I produced for the community. Had more people join the community from platforms like YouTube, LinkedIn & TikTok. More content. More community. And so on. Having your own community means that you have your own target market within our own distribution channel which you can market to at any time whatsoever without having to spend any additional ad spend whatsoever. Additionally, it also gives your prospects the ability to go through a successful buyer journey and engage with your brand/offer FREELY until they're ready to buy or commit to you. What I mean by this is that not everybody is ready to buy from you today. Some people are just interested in learning about what you're offering. If you have a community, they can join it, spend a few days interacting both with your brand and other community members, increase their level of trust to you/your brand. If they successfully go through this buyer journey whilst being in your community there's no doubt whatsoever that you'll be in their top 1% of preferred vendors as they trust you and know you. ALREADY BUILD A COMMUNITY. Or at least try to. Growth Marketing Strategy #4 - TikTok Ads TikTok has been growing at an alarming rate over the past couple of years. As a matter of fact, it's been growing so alarmingly fast that majors companies like Instagram and Youtube felt threatened to the extent that they even had to launch their own variation of a short-form content feed. This led to Instagram & Facebook launching Reels, and Youtube launching what's known as Youtube Shorts. Direct copycat variations of TikTok. Everybody knows this, but what most companies seem to be forgetting is the TikTok Ad Platform. Here's how I took advantage of it: One of the latest tests that I'd run for both the agency & the Growth Hacking Bootcamp is to test whether I'd be able to generate leads at a cost-effective rate within the Solar Installation industry at a cost-effective rate. TikTok was one of the platforms that I wanted to test running ads for quite a while, so I thought why not test out this offer on the TikTok audience? And so I did. I created a quick script for the TikTok Ad itself. I made sure that my script included a hook & a pitch to follow it. I went on Fiverr to hire a paid spokesperson specifically for TikTok Ads. I sent them the script. They sent me over the raw video of them saying exactly what I want them to say. I opened up the raw video in Premiere Pro, subtitled it, and added some relevant b-roll content and some dynamic background music from Artlist.’ And voila, that was my TikTok Ad. I then opened up the TikTok Ad Manager, uploaded my ad, connected to my ad account (the software that would push the leads from TikTok to a Google Sheet via API), and set the ad to run on a budget of €30 a day for a few days targeting men & women within the UK aged between 35 to 55. LeadsBridge The result? 7 leads of people interested in installing solar within their households for a total spend of 250. Total CPL of €35. TikTok is definitely an ad platform worth checking out. Growth Marketing Strategy #5 - Cold Email Automated Outreaches Automated Cold Email is one of the most cost-effective B2B & B2C marketing strategies out there. And yes, it's applicable to the B2C sector too when you're aim is to onboard influencers & celebrities that are going to promote your product to their audience. The issue though with most attempted automated cold email outreaches is that the setup is all wrong. This results in most emails being TOTALLY ignored as they just hit the spam box. Resulting in a waste of money, time & domain deliverability. What the experts consider: Cold Email is an art. And every art has its way. A successful cold email strategy is much more than just connecting your primary inbox to some automation tool and hoping for the best. Instead, a successful automated cold email outreach also takes the following elements into account: Satellite Domains Records Provider Automation Tool Warming Tool Rotation Cycle List Copy are essentially the domains you're going to be emailing from. And no, you never do cold email outreaches from your main domain as this will result in you eventually hitting spam, being blacklisted, and possibly even having your website delisted. Satellite domains: Examples of satellite domains include the following: Say you own magicagency(.)com and that's your main domain. A collection of relevant satellite domains would include: magicagency(.)co magicagency(.)biz magicagencyteam(.)com etc. The point is that you want to set up relevant domains that will take the heat for your cold email outreach. You'll warm the up, and those will be the key domains that you end up sending emails from, whilst savouring your main domain's deliverability for warm email marketing targeting users that have actually opted into your communications. Once you purchase your satellite domains, you'll want to set up the necessary DKIM, SPF & DMARC records necessary for utmost deliverability. Records: These records let ISPs know that your domains are set up correctly and are immune to malicious practices like spoofing and other forms of spamming. Who will host your inboxes & how will this decision affect your cold email outreach? Provider: Will you let Gsuite host the entire campaign and suffer poor deliverability when it comes to sending to Outlook inboxes? Will you host on Outlook and suffer poor deliverability when it comes down to sending to Gmails & other Gsuite domains? It's a serious decision that needs to be made. Once you've decided on the above elements, you've also got to make a decision on which automation tool you'll be using for the cold email outreach. Automation Tool: Will you be using Instantly? Lemlist? Snov.io? This decision will have an effect on your cost per email sent & your overall deliverability as no two tools are made the same. Deliverability is key when it comes down to Cold Email Outreaches. Warming Tool: And it's a pretty simple concept - it's your email's overall ability to land in your prospect's inbox. The question is, how do you improve deliverability? The answer is simple. Warming Tools. Warming tools essentially fake positive email interactions over a given period of time with a wide array of inboxes from numerous providers. This tricks the system into thinking that you should be granted a much higher deliverability than you already are allowed. It's a fact that when you expose any domain to any cold email outreach you'll see its deliverability drop over time. Rotation Cycle: So what do you do once a domain starts hitting spam consistently? Do you forget about it? No, you plug back it back into warming. Hence, a rotation cycle. A pre-planned inter-changeable cycle that says that an X Group of domains will be outreaching for a specified number of days whilst a Y Group of domains will be in warming for a specified number of days. These are some of the key elements experts take into account when launching successful automated mass cold email outreach domains. For more on it make sure to check out our updated , the Growth Hacking Bootcamp. Cold Email module in our Growth Hacking Course Growth Marketing Strategy #6 - Reddit Content Marketing Reddit is the internet's biggest collection of online forums for EVERYTHING! They've got subreddits for just about ANY interest out there. Reddit has r/accounting for accountants looking to discuss anything accounting related. They've got r/vegan for vegans & aspiring vegans to discuss anything related to the topic of veganism. And much more! So the question is, why not use this to your benefit? Here's how I did it: As part of the community-building campaign of our Growth Hackers Inc. groups on WhatsApp, Telegram & WhatsApp, I first had to identify all relevant subreddits related to what we're offering. After some research I realized that my most relevant subreddits were the following: r/marketing r/growthhacking r/digitalmarketing r/entrepreneurialridealong Next up, I had to find out what type of content the aforementioned subreddits liked. To do this, I simply researched the content that received the most upvotes within the past year. Once I realized what clicked within my targeted subreddits, my mission became very simple. I had to create content alike to the content I'd researched previously and make sure I adhere to the best practises of every individual subreddit. And so I did. Here are some results: with 196 upvotes, 36 comments & a Reddit award. One post , tons of reach & another award. Another with 53 upvotes And so on. Lastly, here's an extra Growth Marketing Strategy you should never forget… Never stop testing! Constant testing is the backbone of any successful growth marketing strategy. Just make sure that your analytics are set up right so that you can analyze both your successes & failures equally and it won't be long till you discover a strategy with hockey stick growth potential that's worth doubling down on. Hope you enjoyed this article.

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List of the Best Mobile App Development Companies & Developers — 2018 Reviews Is it a really harder for you to find best App Development company from a collection of thousands or maybe more?To save you from such a striving task, I collected here a prime list of the best app development companies in india. They have made business-growing apps since years and still making them with latest introduced technology. All of them are the solid ground of the best mobile app developers with exceptional technical skills plus heart winning strategies. So, what are you waiting for? Let’s start with the list of the best app development Companies in India: Best 10+ app development Companies in India 2018 IndiaNIC Infotech Ltd IndiaNIC is a leading mobile app, web and mobile games development company. Since 1998, we have successfully bagged 7000+ projects, building lifetime relationships with over 3500+ happy clients, for providing IT solutions to every industry sector out there. With the employee strength of over 400 mobile & web developers, designers and marketing experts working from our head office in Ahmedabad, Gujarat, we hail from a background of specialising in Mobile Apps, Games, Web Portal, Ecommerce, Custom Business Applications, Software Development using latest technologies as per the current and changing trends. Our expertise lies in Open Source Technologies, CMS’s, Microsoft Technologies, JAVA, MEAN Stake, iOS, Android, XAMARIN and other related trending tools and technologies. We are an ISO certified company and member of NASSCOM. Our client list includes names like Adidas, Cisco, Pepsi, Vodafone, McDonald’s, Gulfstream, Incentex, Jackson & Coker, Sancho BBDO, TATA, Kotak Mahindra Bank, Nirma Limited, and many more. Pricing: < $25/hr Location: India Employees: 250–999 Founded: 1998 2. Peerbits Peerbits is a global IT solution company delivering web development, mobile application development and IT consulting services of any complexity to clients worldwide. Peerbits has a strong team of 50+ highly skilled IT experts. Peerbits carries trust of more than 200+ clients spread across diverse business verticals.We are committed to excellence through the systematic and disciplined process. Our expertise guarantees quality, competence and rapid execution of any projects ranging from mid-sized portals to complex app development and custom mobility solutions.Our vision is to provide the utmost integrity and client satisfaction through our services, creating lasting relationships with our clients. Pricing: < $25/hr Location: India Employees: 50–249 Founded: 2011 3. Quytech An award winning bespoke mobile app development agency quy technology pvt ltd providing enterprise mobility solution, chat application, Augmented reality application & cross platform services . We have been rated among the top 20 most promising enterprise mobility company by CIO review. Pricing: < $25/hr Location: India Employees: 50–249 Founded: 2010 4. Finoit Finoit is a product engineering Company providing strategic and technology consulting in the field of Mobile, Software and IoT domain. We are focused on designing and developing customer-centric products and applications for startups to elite business groups across the globe. With our technology consulting and implementation services using Web, Mobile and IoT development technologies, we have delivered intuitive digital experiences and scalable products while solving business problems to over 250 customers across North America, Europe and Australia. Pricing: < $25/hr Location: India Employees: 50–249 Founded: 2010 5. Hopinfirst Hopinfirst is a leading edge custom software development, web and mobile offers development of websites, mobile applications, AR/AR development, Digital marketing services app development company Pricing: < $25/hr Location: India Employees: 10–49 Founded: 2016 6. konstantinfo Konstant Infosolutions is an industry leader in delivering successful Mobile and Web development solutions across the Globe. We have been working since 2003, and have 2500+ happy customers worldwide. Having delivered 3500+ successful applications, we have served 30+ large Enterprises as well as start-ups. We are committed to success and our endeavor is to build highly performant and cost-efficient solutions. We have partnered with distinguished industries and firms worldwide like Amazon Web Services, Member of iOS Developer Program, Registered Google Play Developer, Microsoft Certified Partner, Adobe Solution Partner, Intel Partner, Acquia Partner, Authorize.Net Pricing: < $25/hr Location: India Employees: 50–249 Founded: 2003 7. Octalsoftware Octal IT Solution is a global IT service provider having a strong presence in UK, USA, Singapore and India. The company has the reputation of delivering niche solutions in the areas of web and mobile app development. Octal has repeatedly prescribed quality as its virtue. With the ISO 9001:2008 certification and strict discipline towards the prominent quality standards, the company has produced flawless results since last decade. In its history of ten years, Octal has earned notable associations from technology leaders like Microsoft, NASSCOM, Amazon Web Services, Acquia, STPI, Dun & Bradstreet and Drupal Organization etc. Pricing: < $25/hr Location: India Employees: 50–249 Founded: 2007 8. OpenXcell Incorporated in 2008 and headquartered in the USA, OpenXcell is an industry-leading software and mobile app development company known for delivering innovative software solutions and engaging mobile apps. Due to our unstoppable quest for making perfect mobile and web apps, we have slowly evolved into a one stop destination for all mobile and web app development needs. We have made a stellar reputation in the technology industry by adhering to strict timelines and quality deliverables. We are considered to be one of the most reliable partners by our clients as we follow a customer-oriented service model. Pricing: < $25/hr Location: India Employees: 50–249 Founded: 2009 9. 360 Degree Technosoft 360 Degree Technosoft is global solution provider of web development and mobile applications development dealing in various platforms like iOS and android. Having developed more than 500+ mobile applications on various platforms, 360 Degree Technosoft is well-accustomed with Google and Apple tools desired to develop a mobile application. Pricing: < $25/hr Location: India Employees: 10–49 Founded: 2010 10. Spaceotechnologies Space-O is one stop solution for all your mobile software development needs. From concept to development to marketing to ongoing maintenance, Space-O delivers. We are now one of the top mobile app development companies in India by following the success mantra of “Design-led-Engineering”. We work with best-of-the-best fine art grads from top design institutes such as NID and best-of-the-best engineers. Pricing: $25 — $49/hr Location: India Employees: 250–999 Founded: 2010 11. Cumulations Technologies Cumulations Technologies is Bangalore based most trusted mobile app development company serving to the industry from past 8 years. Delivered 100+ projects for more than 70 clients. Specialty lies in solving challenging problems and converting to a viable solution. More than 95% of Projects delivered on time. Worked in almost every industry and niche including smart home, healthcare, e-commerce, ticket booking, Wearable devices, Fintech and more. Pricing: $25 — $49/hr Location: India Employees: 10–49 Founded: 2010 12. Arka Softwares Arka Softwares is the one-stop solutions for all your mobile app requirements. From developing websites for industry giants to providing seamless user experience, there are plethoras of reasons that you can find to hire Arka Softwares as your development partner. With a team of 100+ developers and designers, Arka Softwares have delivered more than 100+ mobile apps to clients all across the globes. With a primary focus on agile development methodology, Arka Softwares offers great benefits to all the clients. As a custom mobile app development company, we provide robust, scalable and high-end development solutions to all our clients from small start-ups to large enterprise. Pricing: 18$ to 20$/hr Location: India, USA, UK Employee: 100+ Founded: 2010 These are the best app development companies in India that you can look out. You can also give me your suggestions in the comment box below. PS: This list is in progress and I update it every fortnight. Would you Like to list your App Development Company? Please do comment with your company name.

Best 10+ app development companies in India

Table of Links Abstract and 1. Introduction Background and Related Work


Threat Model


Robust Style Mimicry


Experimental Setup


Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis


Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples B. Robust Mimicry Generations C. Detailed Results D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 F. Findings on Mist v2 G. Methods for Style Mimicry H. Existing Style Mimicry Protections I. Robust Mimicry Methods J. Experimental Setup K. User Study L. Compute Resources J Experimental Setup This section describes our general experimental setup and specifies the settings and hyperparameters of the methods we use. When possible, we use default values from the machine learning literature. For implementation details see our official repository: https://github.com/ethz-spylab/ robust-style-mimicry J.1 Style Mimicry Experimental Details As described in Section 3, our threat model considers style mimicry with a latent diffusion text-toimage model M that is finetuned on a set of images X in a style S. This section specifies our choices for model M, images X, style S, the hyperparameters for finetuning M, and the hyperparameters for generating images with the finetuned model. Where possible, we try to replicate the style mimicry setup used by Shan et al. to evaluate Glaze, and highlight any differences. Model We use Stable Diffusion version 2.1 (Stability AI, 2022), the same model used to optimize the protections we evaluate (Shan et al., 2023a; Liang et al., 2023; Van Le et al., 2023). J.2 Protections Experimental Details Next, we describe specific hyperparameters we use to reproduce each of the protections. J.2.1 Anti-DreamBooth J.2.2 Mistϕ J.2.3 Glaze The Glaze authors were unable to share a codebase upon request. We thus use their publicly released Windows application binary. We use the latest available version of Glaze, v1.1.1. We set Intensity to High and Render Quality to Slowest, to obtain the strongest protections. Appendix E includes qualitative results on an updated version released after we concluded our user study. J.3 Robust Mimicry Methods Experimental Details J.3.1 Gaussian noising We manually tune the Gaussian noising strength to σ2 = 0.05. J.3.2 DiffPure We use conditional DiffPure with the best-performing publicly available image generation diffusion model, Stable Diffusion XL 1.0 (SDXL) (Podell et al., 2023). We implement conditional DiffPure using the HuggingFace AutoPipelineForImage2Image pipeline. We use classifier-free guidance scale guidance scale = 7.5 with prompt P = Cx for image x. We manually tune the number of diffusion timesteps t via the strength pipeline argument to strength = 0.2. J.3.3 IMPRESS++ Authors:
(1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch);
(2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch);
(3) Nicholas Carlini, Google DeepMind;
(4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. [8] www.artstation.com [9] @nulevoy is the first ArtStation artist that we experimented with. In our experiments, we found “nulevoy” a suitable choice for the special word w∗ and use it for all artists. We check that all of nulevoy’s images are published after the release date of LAION-5B to ensure that SD 2.1 has no prior knowledge about nulevoy’s style. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background and Related Work Threat Model Robust Style Mimicry Experimental Setup Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Background and Related Work Background and Related Work Background and Related Work Threat Model Threat Model Threat Model Robust Style Mimicry Robust Style Mimicry Robust Style Mimicry Experimental Setup Experimental Setup Experimental Setup Results 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis Results Results 6.1 Main Findings: All Protections are Easily Circumvented 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis 6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples A. Detailed Art Examples B. Robust Mimicry Generations B. Robust Mimicry Generations C. Detailed Results C. Detailed Results D. Differences with Glaze Finetuning D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 E. Findings on Glaze 2.0 F. Findings on Mist v2 F. Findings on Mist v2 G. Methods for Style Mimicry G. Methods for Style Mimicry H. Existing Style Mimicry Protections H. Existing Style Mimicry Protections I. Robust Mimicry Methods I. Robust Mimicry Methods J. Experimental Setup J. Experimental Setup K. User Study K. User Study L. Compute Resources L. Compute Resources J Experimental Setup This section describes our general experimental setup and specifies the settings and hyperparameters of the methods we use. When possible, we use default values from the machine learning literature. For implementation details see our official repository: https://github.com/ethz-spylab/ robust-style-mimicry J.1 Style Mimicry Experimental Details As described in Section 3, our threat model considers style mimicry with a latent diffusion text-toimage model M that is finetuned on a set of images X in a style S. This section specifies our choices for model M, images X, style S, the hyperparameters for finetuning M, and the hyperparameters for generating images with the finetuned model. Where possible, we try to replicate the style mimicry setup used by Shan et al. to evaluate Glaze, and highlight any differences. Model We use Stable Diffusion version 2.1 (Stability AI, 2022), the same model used to optimize the protections we evaluate (Shan et al., 2023a; Liang et al., 2023; Van Le et al., 2023). Model J.2 Protections Experimental Details Next, we describe specific hyperparameters we use to reproduce each of the protections. J.2.1 Anti-DreamBooth J.2.1 Anti-DreamBooth J.2.2 Mistϕ J.2.2 Mistϕ J.2.3 Glaze J.2.3 Glaze The Glaze authors were unable to share a codebase upon request. We thus use their publicly released Windows application binary. We use the latest available version of Glaze, v1.1.1. We set Intensity to High and Render Quality to Slowest, to obtain the strongest protections. Appendix E includes qualitative results on an updated version released after we concluded our user study. J.3 Robust Mimicry Methods Experimental Details J.3.1 Gaussian noising J.3.1 Gaussian noising We manually tune the Gaussian noising strength to σ2 = 0.05. J.3.2 DiffPure J.3.2 DiffPure We use conditional DiffPure with the best-performing publicly available image generation diffusion model, Stable Diffusion XL 1.0 (SDXL) (Podell et al., 2023). We implement conditional DiffPure using the HuggingFace AutoPipelineForImage2Image pipeline. We use classifier-free guidance scale guidance scale = 7.5 with prompt P = Cx for image x. We manually tune the number of diffusion timesteps t via the strength pipeline argument to strength = 0.2. J.3.3 IMPRESS++ J.3.3 IMPRESS++ Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). Authors: Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv [8] www.artstation.com [9] @nulevoy is the first ArtStation artist that we experimented with. In our experiments, we found “nulevoy” a suitable choice for the special word w∗ and use it for all artists. We check that all of nulevoy’s images are published after the release date of LAION-5B to ensure that SD 2.1 has no prior knowledge about nulevoy’s style.

How We Evaluated Different AI Techniques for Mimicking Artistic Styles

Table of Links Abstract and 1. Introduction Background and Related Work


Threat Model


Robust Style Mimicry


Experimental Setup


Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis


Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples B. Robust Mimicry Generations C. Detailed Results D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 F. Findings on Mist v2 G. Methods for Style Mimicry H. Existing Style Mimicry Protections I. Robust Mimicry Methods J. Experimental Setup K. User Study L. Compute Resources H Existing Style Mimicry Protections Naming convention. Depending on the context, style mimicry protections may be viewed either as attacks or as the targets of attacks. In an artistic setting, artists see style mimicry as an attack and utilize methods like Glaze as a defense. Conversely, in the context of adversarial robustness, Glaze can be seen as an attack against style mimicry methods through adversarial perturbations. The research community has not reached a consensus on terminology: Glaze’s authors consider style mimicry an attack and label Glaze as a defense, while the authors of Mist and Anti-DreamBooth describe their approaches as attacks. In our work, we distance ourselves from the attack/defense terminology and instead refer to these mechanisms as protections, and to the party performing mimicry as the “style forger”. Existing protections can either target the encoder or the decoder of text-to-image models. We classify them accordingly. H.1 Encoder Protections H.2 Denoiser Protections Denoiser protections use the prediction error of the denoiser ϵθ as a proxy of the quality of style mimicry, making it a feasible target for adversarial optimization. Current Denoiser protections, such as Mist (Liang et al., 2023) and Anti-DreamBooth (Van Le et al., 2023) assume that poorly reconstructed images will fail to mimic style Authors:
(1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch);
(2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch);
(3) Nicholas Carlini, Google DeepMind;
(4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. [7] Mist project also contains a denoiser attack that we fail to reproduce as a robust protection. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background and Related Work Threat Model Robust Style Mimicry Experimental Setup Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Background and Related Work Background and Related Work Background and Related Work Threat Model Threat Model Threat Model Robust Style Mimicry Robust Style Mimicry Robust Style Mimicry Experimental Setup Experimental Setup Experimental Setup Results 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis Results Results 6.1 Main Findings: All Protections are Easily Circumvented 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis 6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples A. Detailed Art Examples B. Robust Mimicry Generations B. Robust Mimicry Generations C. Detailed Results C. Detailed Results D. Differences with Glaze Finetuning D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 E. Findings on Glaze 2.0 F. Findings on Mist v2 F. Findings on Mist v2 G. Methods for Style Mimicry G. Methods for Style Mimicry H. Existing Style Mimicry Protections H. Existing Style Mimicry Protections I. Robust Mimicry Methods I. Robust Mimicry Methods J. Experimental Setup J. Experimental Setup K. User Study K. User Study L. Compute Resources L. Compute Resources H Existing Style Mimicry Protections Naming convention. Depending on the context, style mimicry protections may be viewed either as attacks or as the targets of attacks. In an artistic setting, artists see style mimicry as an attack and utilize methods like Glaze as a defense. Conversely, in the context of adversarial robustness, Glaze can be seen as an attack against style mimicry methods through adversarial perturbations. The research community has not reached a consensus on terminology: Glaze’s authors consider style mimicry an attack and label Glaze as a defense, while the authors of Mist and Anti-DreamBooth describe their approaches as attacks. In our work, we distance ourselves from the attack/defense terminology and instead refer to these mechanisms as protections, and to the party performing mimicry as the “style forger”. Naming convention. Existing protections can either target the encoder or the decoder of text-to-image models. We classify them accordingly. H.1 Encoder Protections H.2 Denoiser Protections Denoiser protections use the prediction error of the denoiser ϵθ as a proxy of the quality of style mimicry, making it a feasible target for adversarial optimization. Current Denoiser protections, such as Mist (Liang et al., 2023) and Anti-DreamBooth (Van Le et al., 2023) assume that poorly reconstructed images will fail to mimic style Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). Authors: Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv [7] Mist project also contains a denoiser attack that we fail to reproduce as a robust protection.

How Are Artists Protecting Their Unique Styles from Imitation in AI Art?

Table of Links Abstract and 1. Introduction Background and Related Work


Threat Model


Robust Style Mimicry


Experimental Setup


Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis


Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples B. Robust Mimicry Generations C. Detailed Results D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 F. Findings on Mist v2 G. Methods for Style Mimicry H. Existing Style Mimicry Protections I. Robust Mimicry Methods J. Experimental Setup K. User Study L. Compute Resources 6.2 Analysis We now discuss key insights and lessons learned from these results. Glaze protections break down without any circumvention attempt. Results for Glaze without robust mimicry (see “Naive mimicry” row in Figure 4) show that the tool’s protections are often ineffective. Without any robustness intervention, 30% of the images generated with our off-the-shelf finetuning are rated as better than the baseline results using only unprotected images. This contrasts with Glaze’s original evaluation, which claimed a success rate of at most 10% for robust mimicry.[4] This difference is likely due to the protection’s brittleness to slight changes in the finetuning setup (as we illustrated in Section 4.1). With our best robust mimicry method (noisy upscaling) the median success rate across artists rises further to 40%, and our best-of-4 strategy yields results indistinguishable from the baseline for a majority of artists. Robust mimicry works for contemporary and historical artists alike. Shan et al. (2023b) note that one of IMPRESS’ main limitations is that “purification has a limited effect when tested on artists that are not well-known historical artists already embedded in original training data”. Yet, we find that our best-performing robust mimicry method—Noisy Upscaling—has a similar success rate for historical artists (42.2%) and contemporary artists with little representation in the model’s training set (43.5%). Protections are highly non-uniform across artists. As we observe from Figure 4, the effectiveness of protections varies significantly across artists: the least vulnerable artist (left-most whisker) enjoys much stronger mimicry protections than the median artist or the most vulnerable artist (right-most whisker). We find that robust mimicry is the least successful for artists where the baseline mimicry from unprotected images gives poor results to begin with (cf. results for artist A1 in Appendix C and Appendix K.1). Yet, since existing tools do not provide artists with a way to check how vulnerable they are, these tools still provide a false sense of security for all artists. This highlights an inherent asymmetry between protection tools and mimicry methods: protections should hold for all artists alike, while a mimicry method might successfully target only specific artists. Robust mimicry failures still remove protection artifacts. We manually checked the cases where all annotators ranked mimicry from unprotected art as better than robust mimicry with Noisy Upscaling. Figure 5 shows two examples. We find that in many instances, the model fails to mimic the style accurately even from unprotected art. In these cases, robust mimicry is still able to generate clear images that are similar to unprotected mimicry, but neither matches the original style well. Authors:
(1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch);
(2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch);
(3) Nicholas Carlini, Google DeepMind;
(4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. [4] The original evaluation in Glaze directly asks annotators whether a mimicry is successful or not, rather than a binary comparison between a robust mimicry and a baseline mimicry as in our setup. Shan et al. (2023a) report that mimicry fails in 4% of cases for unprotected images, and succeeds in 6% of cases for protected images. This bounds the success rate for robust mimicry—according to our definition in Equation (1)—by at most 10%. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background and Related Work Threat Model Robust Style Mimicry Experimental Setup Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Background and Related Work Background and Related Work Background and Related Work Threat Model Threat Model Threat Model Robust Style Mimicry Robust Style Mimicry Robust Style Mimicry Experimental Setup Experimental Setup Experimental Setup Results 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis Results Results 6.1 Main Findings: All Protections are Easily Circumvented 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis 6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples A. Detailed Art Examples B. Robust Mimicry Generations B. Robust Mimicry Generations C. Detailed Results C. Detailed Results D. Differences with Glaze Finetuning D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 E. Findings on Glaze 2.0 F. Findings on Mist v2 F. Findings on Mist v2 G. Methods for Style Mimicry G. Methods for Style Mimicry H. Existing Style Mimicry Protections H. Existing Style Mimicry Protections I. Robust Mimicry Methods I. Robust Mimicry Methods J. Experimental Setup J. Experimental Setup K. User Study K. User Study L. Compute Resources L. Compute Resources 6.2 Analysis We now discuss key insights and lessons learned from these results. Glaze protections break down without any circumvention attempt. Results for Glaze without robust mimicry (see “Naive mimicry” row in Figure 4) show that the tool’s protections are often ineffective. Without any robustness intervention, 30% of the images generated with our off-the-shelf finetuning are rated as better than the baseline results using only unprotected images. This contrasts with Glaze’s original evaluation, which claimed a success rate of at most 10% for robust mimicry.[4] This difference is likely due to the protection’s brittleness to slight changes in the finetuning setup (as we illustrated in Section 4.1). With our best robust mimicry method (noisy upscaling) the median success rate across artists rises further to 40%, and our best-of-4 strategy yields results indistinguishable from the baseline for a majority of artists. Glaze protections break down without any circumvention attempt. Robust mimicry works for contemporary and historical artists alike. Shan et al. (2023b) note that one of IMPRESS’ main limitations is that “purification has a limited effect when tested on artists that are not well-known historical artists already embedded in original training data”. Yet, we find that our best-performing robust mimicry method—Noisy Upscaling—has a similar success rate for historical artists (42.2%) and contemporary artists with little representation in the model’s training set (43.5%). Robust mimicry works for contemporary and historical artists alike. Protections are highly non-uniform across artists. As we observe from Figure 4, the effectiveness of protections varies significantly across artists: the least vulnerable artist (left-most whisker) enjoys much stronger mimicry protections than the median artist or the most vulnerable artist (right-most whisker). We find that robust mimicry is the least successful for artists where the baseline mimicry from unprotected images gives poor results to begin with (cf. results for artist A1 in Appendix C and Appendix K.1). Yet, since existing tools do not provide artists with a way to check how vulnerable they are, these tools still provide a false sense of security for all artists. This highlights an inherent asymmetry between protection tools and mimicry methods: protections should hold for all artists alike, while a mimicry method might successfully target only specific artists. Protections are highly non-uniform across artists. Robust mimicry failures still remove protection artifacts . We manually checked the cases where all annotators ranked mimicry from unprotected art as better than robust mimicry with Noisy Upscaling. Figure 5 shows two examples. We find that in many instances, the model fails to mimic the style accurately even from unprotected art. In these cases, robust mimicry is still able to generate clear images that are similar to unprotected mimicry, but neither matches the original style well. Robust mimicry failures still remove protection artifacts Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). Authors: Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv [4] The original evaluation in Glaze directly asks annotators whether a mimicry is successful or not, rather than a binary comparison between a robust mimicry and a baseline mimicry as in our setup. Shan et al. (2023a) report that mimicry fails in 4% of cases for unprotected images, and succeeds in 6% of cases for protected images. This bounds the success rate for robust mimicry—according to our definition in Equation (1)—by at most 10%.

The Struggle to Stop AI from Imitating Human Artists' Styles

Table of Links Abstract and 1. Introduction Background and Related Work


Threat Model


Robust Style Mimicry


Experimental Setup


Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis


Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples B. Robust Mimicry Generations C. Detailed Results D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 F. Findings on Mist v2 G. Methods for Style Mimicry H. Existing Style Mimicry Protections I. Robust Mimicry Methods J. Experimental Setup K. User Study L. Compute Resources 6 Results In Figure 4, we report the distribution of success rates per artist (N=10) for each scenario. We averaged the quality and stylistic transfer success rates to simplify the analysis (detailed results can be found in Appendix C). Since the forger can try multiple mimicry methods for each prompt, and then decide which one worked best, we also evaluate a “best-of-4” method that picks the most successful mimicry method for each generation (according to human evaluators). Authors:
(1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch);
(2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch);
(3) Nicholas Carlini, Google DeepMind;
(4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background and Related Work Threat Model Robust Style Mimicry Experimental Setup Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Background and Related Work Background and Related Work Background and Related Work Threat Model Threat Model Threat Model Robust Style Mimicry Robust Style Mimicry Robust Style Mimicry Experimental Setup Experimental Setup Experimental Setup Results 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis Results Results 6.1 Main Findings: All Protections are Easily Circumvented 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis 6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples A. Detailed Art Examples B. Robust Mimicry Generations B. Robust Mimicry Generations C. Detailed Results C. Detailed Results D. Differences with Glaze Finetuning D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 E. Findings on Glaze 2.0 F. Findings on Mist v2 F. Findings on Mist v2 G. Methods for Style Mimicry G. Methods for Style Mimicry H. Existing Style Mimicry Protections H. Existing Style Mimicry Protections I. Robust Mimicry Methods I. Robust Mimicry Methods J. Experimental Setup J. Experimental Setup K. User Study K. User Study L. Compute Resources L. Compute Resources 6 Results In Figure 4, we report the distribution of success rates per artist (N=10) for each scenario. We averaged the quality and stylistic transfer success rates to simplify the analysis (detailed results can be found in Appendix C). Since the forger can try multiple mimicry methods for each prompt, and then decide which one worked best, we also evaluate a “best-of-4” method that picks the most successful mimicry method for each generation (according to human evaluators). Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). Authors: Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

How Easy Is It for AI To Mimic a Human Artist’s Style?

Table of Links Abstract and 1. Introduction Background and Related Work


Threat Model


Robust Style Mimicry


Experimental Setup


Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis


Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples B. Robust Mimicry Generations C. Detailed Results D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 F. Findings on Mist v2 G. Methods for Style Mimicry H. Existing Style Mimicry Protections I. Robust Mimicry Methods J. Experimental Setup K. User Study L. Compute Resources 3 Threat Model The goal of style mimicry is to produce images, of some chosen content, that mimic the style of a targeted artist. Since artistic style is challenging to formalize or quantify, we refrain from doing so and define a mimicry attempt as successful if it generates new images that a human observer would qualify as possessing the artist’s style. We assume two parties, the artist who places art online (e.g., in their portfolio), and a forger who performs style mimicry using these images. The challenge for the forger is that the artist first protects their original art collection before releasing it online, using a state-of-the-art protection tool such as Glaze, Mist or Anti-DreamBooth. We make the conservative assumption that all the artist’s images available online are protected. If a mimicry method succeeds in this setting, we call it robust. In this work, we consider style forgers who finetune a text-to-image model on an artist’s images—the most successful style mimicry method to date (Shan et al., 2023a). Specifically, the forger finetunes a pretrained model f on protected images X from the artist to obtain a finetuned model ˆf. The forger has full control over the protected images and finetuning process, and can arbitrarily modify to maximize the mimicry success. Our robust mimicry methods combine a number of “off-the-shelf” manipulations that allow even low-skilled parties to bypass existing style mimicry protections. In fact, our most successful methods require only black-box access to a finetuning API for the model f, and could thus also be applied to proprietary text-to-image models that expose such an interface. Authors:
(1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch);
(2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch);
(3) Nicholas Carlini, Google DeepMind;
(4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background and Related Work Threat Model Robust Style Mimicry Experimental Setup Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Background and Related Work Background and Related Work Background and Related Work Threat Model Threat Model Threat Model Robust Style Mimicry Robust Style Mimicry Robust Style Mimicry Experimental Setup Experimental Setup Experimental Setup Results 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis Results Results 6.1 Main Findings: All Protections are Easily Circumvented 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis 6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples A. Detailed Art Examples B. Robust Mimicry Generations B. Robust Mimicry Generations C. Detailed Results C. Detailed Results D. Differences with Glaze Finetuning D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 E. Findings on Glaze 2.0 F. Findings on Mist v2 F. Findings on Mist v2 G. Methods for Style Mimicry G. Methods for Style Mimicry H. Existing Style Mimicry Protections H. Existing Style Mimicry Protections I. Robust Mimicry Methods I. Robust Mimicry Methods J. Experimental Setup J. Experimental Setup K. User Study K. User Study L. Compute Resources L. Compute Resources 3 Threat Model The goal of style mimicry is to produce images, of some chosen content, that mimic the style of a targeted artist. Since artistic style is challenging to formalize or quantify, we refrain from doing so and define a mimicry attempt as successful if it generates new images that a human observer would qualify as possessing the artist’s style. We assume two parties, the artist who places art online (e.g., in their portfolio), and a forger who performs style mimicry using these images. The challenge for the forger is that the artist first protects their original art collection before releasing it online, using a state-of-the-art protection tool such as Glaze, Mist or Anti-DreamBooth. We make the conservative assumption that all the artist’s images available online are protected. If a mimicry method succeeds in this setting, we call it robust. In this work, we consider style forgers who finetune a text-to-image model on an artist’s images—the most successful style mimicry method to date (Shan et al., 2023a). Specifically, the forger finetunes a pretrained model f on protected images X from the artist to obtain a finetuned model ˆf. The forger has full control over the protected images and finetuning process, and can arbitrarily modify to maximize the mimicry success. Our robust mimicry methods combine a number of “off-the-shelf” manipulations that allow even low-skilled parties to bypass existing style mimicry protections. In fact, our most successful methods require only black-box access to a finetuning API for the model f, and could thus also be applied to proprietary text-to-image models that expose such an interface. Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). Authors: Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

How AI Forgers Bypass Style Protections to Mimic Artists' Work

In a bold move highlighting growing tensions between artists and AI developers, a protest group known as "Sora PR Puppets" has leaked access to OpenAI's unreleased text-to-video model, Sora, on the Hugging Face platform. This act of defiance sheds light on alleged exploitative practices within OpenAI's early access program and offers a rare glimpse into the capabilities of the Sora model. Understanding Sora: OpenAI's Text-to-Video Model Sora is OpenAI's advanced text-to-video model designed to generate high-quality videos from textual descriptions. Building upon the technologies behind DALL·E and GPT models, Sora utilizes a recaptioning technique to produce videos that closely adhere to user prompts. The model is capable of creating complex scenes featuring multiple characters, specific motions, and detailed backgrounds, demonstrating an understanding of physical world dynamics. The Leak and Its Implications The leaked version of Sora allowed users to generate 1080p, 10-second video clips, reportedly at faster render times than previously disclosed. Notably, these clips bore OpenAI's watermark, indicating their origin. The temporary availability of Sora on Hugging Face lasted several hours before OpenAI intervened to shut down access. https://x.com/btibor91/status/1861433835528839632?embedable=true Artists' Grievances: Unpaid Labor and Strict Controls The protest group claims that OpenAI enlisted hundreds of artists for unpaid testing, maintaining strict control over all generated content and its dissemination. In an open letter accompanying the leak, the artists stated: "We received access to Sora with the promise to be early testers, red teamers, and creative partners. However, we believe instead we are being lured into 'art washing' to tell the world that Sora is a useful tool for artists." The Verge They further criticized the program as being more about public relations than genuine creative collaboration. OpenAI's Response and Ongoing Developments In response to the leak, OpenAI emphasized that participation in its research preview was voluntary, with no obligation to provide feedback or use the tool. The company stated: "Sora is still in research preview, and we're working to balance creativity with robust safety measures for broader use." The Verge Reports suggest that OpenAI has been training a new version of Sora to address long render times, with rumored features like in-painting and image generation. Industry Context: A Glimpse into AI Video Generation While competitors race to ship AI video tools, Sora has remained behind closed doors, making this leak a dramatic first look at OpenAI's progress. The capabilities seem impressive but do not leap ahead of the competition, and this leak may expose some underlying tension with the creatives they depend on for testing. https://x.com/slow_developer/status/1861440783867388298?embedable=true Conclusion: A Call for Ethical Collaboration The "Sora PR Puppets" leak underscores the need for ethical collaboration between AI developers and the creative community. As AI-generated content becomes more prevalent, establishing fair and transparent practices is crucial to ensure that technological advancements benefit all stakeholders involved. In a bold move highlighting growing tensions between artists and AI developers, a protest group known as "Sora PR Puppets" has leaked access to OpenAI's unreleased text-to-video model, Sora, on the Hugging Face platform. This act of defiance sheds light on alleged exploitative practices within OpenAI's early access program and offers a rare glimpse into the capabilities of the Sora model. Understanding Sora: OpenAI's Text-to-Video Model Understanding Sora: OpenAI's Text-to-Video Model Sora is OpenAI 's advanced text-to-video model designed to generate high-quality videos from textual descriptions. Building upon the technologies behind DALL·E and GPT models, Sora utilizes a recaptioning technique to produce videos that closely adhere to user prompts. The model is capable of creating complex scenes featuring multiple characters, specific motions, and detailed backgrounds, demonstrating an understanding of physical world dynamics. OpenAI The Leak and Its Implications The Leak and Its Implications The leaked version of Sora allowed users to generate 1080p, 10-second video clips, reportedly at faster render times than previously disclosed. Notably, these clips bore OpenAI's watermark, indicating their origin. The temporary availability of Sora on Hugging Face lasted several hours before OpenAI intervened to shut down access. https://x.com/btibor91/status/1861433835528839632?embedable=true https://x.com/btibor91/status/1861433835528839632?embedable=true Artists' Grievances: Unpaid Labor and Strict Controls Artists' Grievances: Unpaid Labor and Strict Controls The protest group claims that OpenAI enlisted hundreds of artists for unpaid testing, maintaining strict control over all generated content and its dissemination. In an open letter accompanying the leak, the artists stated: "We received access to Sora with the promise to be early testers, red teamers, and creative partners. However, we believe instead we are being lured into 'art washing' to tell the world that Sora is a useful tool for artists." "We received access to Sora with the promise to be early testers, red teamers, and creative partners. However, we believe instead we are being lured into 'art washing' to tell the world that Sora is a useful tool for artists." The Verge The Verge The Verge They further criticized the program as being more about public relations than genuine creative collaboration. OpenAI's Response and Ongoing Developments OpenAI's Response and Ongoing Developments In response to the leak, OpenAI emphasized that participation in its research preview was voluntary, with no obligation to provide feedback or use the tool. The company stated: "Sora is still in research preview, and we're working to balance creativity with robust safety measures for broader use." "Sora is still in research preview, and we're working to balance creativity with robust safety measures for broader use." The Verge The Verge The Verge Reports suggest that OpenAI has been training a new version of Sora to address long render times, with rumored features like in-painting and image generation. Industry Context: A Glimpse into AI Video Generation Industry Context: A Glimpse into AI Video Generation While competitors race to ship AI video tools, Sora has remained behind closed doors, making this leak a dramatic first look at OpenAI's progress. The capabilities seem impressive but do not leap ahead of the competition, and this leak may expose some underlying tension with the creatives they depend on for testing. https://x.com/slow_developer/status/1861440783867388298?embedable=true https://x.com/slow_developer/status/1861440783867388298?embedable=true Conclusion: A Call for Ethical Collaboration Conclusion: A Call for Ethical Collaboration The "Sora PR Puppets" leak underscores the need for ethical collaboration between AI developers and the creative community. As AI-generated content becomes more prevalent, establishing fair and transparent practices is crucial to ensure that technological advancements benefit all stakeholders involved.

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