Identity.org

Film has never been afraid of new technology. Sound, color, CGI, each one changed the industry and each one became just another tool. Peter Jackson recently called AI exactly that, just another special effect. His position was clear: if the rights have been licensed from the person whose likeness is being used, he sees no issue. What he objects to is when someone's face or voice ends up in something they never agreed to be part of. Most of the debate around AI in entertainment misses that entirely. It tends to land in one of two places, either the technology should not be used at all, or resistance to it is pointless. The reality is simpler. The same tool can produce something legitimate or cause real harm, and what determines which one is whether the person being recreated had any say in it. variety.com/2026/film/fest…

When OpenAI released the ChatGPT Images 2.0 system card in April, the company acknowledged that the model's photorealistic outputs could, without safeguards, enable more convincing deepfakes of real people and events. One of the leading AI labs put that on record about its own product. According to Chainalysis, the average AI-assisted crypto scam now nets around $3.2 million, roughly four and a half times what a conventional scheme produces, and the tools behind them are consumer products available to anyone with a subscription. The results are already showing up in real cases. A crypto founder recently joined what appeared to be a routine Teams call with a known contact. The face matched, the voice matched, colleagues were present. The call was entirely fabricated and his laptop was compromised before he realized anything was wrong. The credibility of these scams has gone up considerably while the effort required to execute them has come down. cryptoslate.com/ai-scams-in-cr…

McConaughey made an argument at a town hall earlier this year that more people in this space should be making. "Own yourself, voice, likeness, whatever you gotta do, so when it comes, no one can steal you." The word that matters there is own. Not protect, not defend, not fight back. Own. If someone's voice, face, or likeness is being used to make money or build a product, that person should have the right to say yes or no, and if they say yes, they should benefit from it. There is no convincing reason it should work differently here than it does with any other form of ownership. Right now the default runs the other way. Likenesses get used, money gets made, and the person it belongs to is the last to know. variety.com/2026/digital/n…

Major platforms have had a year to prepare, and the deadline is now here. The Take It Down Act requires covered platforms to remove nonconsensual intimate images, including AI-generated deepfakes, within 48 hours of a reported request. This past week the FTC sent compliance letters to more than a dozen companies, including Meta, TikTok, Snapchat, Reddit, Apple, Microsoft, and X. The 48-hour window is where the real work is. Platforms handling millions of uploads daily need systems that can receive, review, and act on requests quickly, and for smaller platforms without the same resources, building that reliably is a much heavier ask. Claiming compliance on paper is one thing. What will be more telling is how these systems hold up once they are actually running.

More than $1.1 billion in deepfake fraud losses have come from fake celebrity investment endorsements, which accounts for more than half of all reported deepfake fraud losses globally. A familiar face creates immediate credibility, and by the time people realize the video was fabricated, the money is often already gone. The financial damage hits victims directly, but the person being impersonated carries a different kind of cost. Their name and credibility get attached to a scam they never participated in, and that association is difficult to undo once it has reached millions of people. Deepfakes are widely treated as an entertainment or misinformation problem. A large and growing portion of the real-world harm they cause is financial, and celebrity impersonation is the primary vehicle driving those losses.

Kentucky has synthetic media disclosure laws on the books. During an actual congressional primary this year, one ad showed a congressman holding hands with political figures he has publicly opposed for years. This is the problem lawmakers keep running into with AI disclosure legislation. Passing a law and enforcing it at the speed content travels online are two entirely different challenges. By the time manipulated content gets flagged, labeled, or debunked, most people have already seen it, reacted, and moved on. The more difficult question is what meaningful enforcement looks like once content is already circulating at scale. Right now, most of the response is still happening well after the damage is done. lpm.org/news/2026-05-0…

Not long ago, AI-generated video was relatively easy to identify. Movement looked unnatural, audio sometimes drifted out of sync, and faces produced small distortions that gave the content away on closer inspection. Most people could catch it. Tools like Sora 2 and Google's Veo 3 have changed that significantly. Both can produce polished, convincing video in minutes, and neither requires technical expertise or expensive software to use. Any consumer with access to the tools can generate footage that most people scrolling past would not stop to question. When OpenAI first released Sora, realistic videos using recognizable public figures spread almost immediately. Most were taken down quickly, but the episode showed how capable the technology had already become. What exists now is considerably more advanced and considerably more accessible. The detection side of this has not kept pace. Most systems still used to identify manipulated content were built for an environment where producing convincing fakes required real skill and time. Updating those systems to reflect what the tools can do now is an open and pressing problem.

For years, Identity.org was focused on privacy-first identity solutions. The core belief was always the same: people should be in control of their own digital presence. That hasn't changed, but the world around it has. AI systems are now being trained on people's faces, voices, and likenesses, often without clear consent or transparency. The questions around who owns your identity and what rights you have are still being worked out, and they matter to a lot of people. So we've refocused the site to cover exactly that. We'll be writing about the news, the legislation, and the real-world cases that are shaping how digital identity gets treated in the age of AI. If that's something you want to follow, subscribe to our blog at identity.org. We'll share our take as things develop.

At this year's Met Gala, fake photos of Nicki Minaj reached millions of views before enough people flagged them. Lady Gaga and Dua Lipa were portrayed as attending an event they never went to, and fabricated Kendall Jenner images spread across platforms before most people caught on. Katy Perry walked in wearing a glove with six fingers, one of the most recognized visual markers people associate with AI-generated imagery. After fake Met Gala photos of her went viral two years running, the detail read as a direct reference to the problem. Public figures have started responding to AI impersonation in ways that are hard to ignore. Beyond what Perry wore, celebrities have pursued lawsuits, filed trademark claims, and pushed platforms to act. The response has moved from private frustration to public action, and this year's Met Gala put that on full display. cosmopolitan.com/style-beauty/f…

For the first time in the United States, someone has been convicted under a federal law specifically written to address AI-generated nonconsensual imagery. A man from Ohio pleaded guilty this month after using AI tools to fabricate explicit images of real people and use them to harass multiple women. For years, situations like this had almost no legal recourse. The Take It Down Act was the first federal law built for this, and this is the first time it has been enforced. Regulations still have a long way to go, but a first conviction matters. It establishes that fabricating someone's image to cause harm is a federal crime, and that people can be held accountable for it. 🔗:nytimes.com/2026/04/08/us/…

Last week we wrote about YouTube opening its deepfake detection tool to all public figures. The announcement got a lot of attention, and for good reason. YouTube has 2.85 billion monthly active users and has paid out more than $70 billion to creators over the last three years. For a platform that size to decide that protecting someone's likeness is its responsibility, not just the individual's, is a real shift in how this is being handled. Most platforms have not done anything close to this. The problem is far from solved, and a lot of people still fall outside what the tool covers. But when a platform this size moves in a particular direction, others have a harder time making the case that it cannot be done. 🔗 Read our latest blog to go deeper on what this signals. identity.org/youtube-signal…

Your face is yours. Your voice is yours. The way you look, sound, and carry yourself belongs to you in the most fundamental sense. But legally, commercially, and across most of the systems that now use identity to generate content and value, that ownership is not guaranteed. It has to be fought for, platform by platform, case by case. Ownership of your voice. Ownership of your face. Ownership of your likeness in every system that uses it. Ownership of the value it creates, with or without your involvement. That should not be something people have to fight for. It should be built into every system that uses identity from the start.

Taylor Swift just filed three trademark applications with the U.S. Patent and Trademark Office, two covering specific phrases spoken in her own voice and one covering a recognizable image of her on stage. Her likeness has already been used in fake ads, pornographic deepfakes, and AI-generated images suggesting political endorsements she never made. The legal protections that existed before were not built to deal with any of that effectively, and her team knows it. Trademark law covers anything close enough to cause confusion, not just exact copies. For someone whose voice and image are being replicated by tools designed to get as close as possible, that broader standard gives her legal team a lot more to work with. Matthew McConaughey pursued the same strategy earlier this year. But Swift doing the same changes the scale of the conversation. When an artist with her level of visibility takes this step, it gives other creators a reason and a roadmap to do the same. This will be the start of more. variety.com/2026/music/new…

$2.19 billion. That is the estimated cost of deepfake fraud globally, according to a recent study from cybersecurity company Surfshark, with the United States alone accounting for $712 million of that total. What stands out is not just the size of the number, but how people are being convinced. More than half of those losses came from fake investment content. Videos that look legitimate, often featuring familiar faces, presenting opportunities that feel credible enough for people to act on. It does not take much. A recognizable face, a believable message, and the ability to distribute it widely. For most people, there is no clear signal that anything is off. The same techniques are showing up elsewhere too, from executives being impersonated to approve transactions, to deepfakes being used in fraudulent loan applications, to entirely fabricated relationships in romance scams. The financial impact is already significant and continuing to grow, but the harder question is what happens beyond the losses. What it does to decision-making, to trust, and to the basic assumption that what you are seeing is real. Because once that starts to break, everything built on top of it becomes harder to rely on. surfshark.com/research/chart…

Imagine finding out that anyone who saw your TikTok video could take your face, your voice, your background, and generate a new image from it. No consent asked. Just a setting that was already switched on. That is TikTok's new meme remixer feature. And opting out means going into every single video individually. There is no account-level toggle. For anyone who posts regularly, that is not a real opt-out. A person's face and voice are tied to how they are perceived and how they earn. That should require a clear yes, not a default that most people will never find. Most people will never opt out. Not because they do not care, but because they will never know they had to. cnet.com/tech/services-…

There is a term for something that can be owned, copied, and used to make money. An asset. Think of real estate, or a patent, or a brand. Your identity is starting to work the same way. A person's face, voice, and likeness can now: • Be copied convincingly without them ever knowing • Show up in videos, ads, and content at any scale • Make money for someone else, with no involvement from the person it belongs to This used to be impossible to do at any meaningful scale. Now it is not. The tools are cheap, widely available, and improving quickly. What stands out is how little exists to manage this. There are no clear systems for tracking usage, no standard way to define boundaries, and no reliable way for people to participate in the value being created from their own identity. The value was always there. What has changed is who can access it, and how easily they can do it without asking.

YouTube just announced it is opening its deepfake detection tool to all public figures, actors, athletes, musicians, and creators, whether or not they have a YouTube channel. This process works by having a person, or someone on their team, upload their likeness into the system. YouTube then scans continuously, flags potential replicas, and puts the decision to respond back in their hands, all at no cost and on an opt-in basis. For anyone whose career is built on their name and face, their image is their reputation and their income. A deepfake does not have to go viral to cause damage. As CAA found during the pilot, by the time AI-generated content featuring a client is discovered, the harm has often already occurred. When a platform at YouTube's scale treats identity protection as a foundational responsibility, it sets an expectation for the rest of the industry. We will start to see more of this. The platforms that get ahead of it will be the ones that treated a person's likeness as something worth protecting before they were required to. hollywoodreporter.com/business/digit…

There is a tendency to describe AI-generated uses of public figures as creative experiments. That framing avoids a more direct question. Was there consent? A recent piece in Campaign Asia explores how brands are using AI to recreate recognizable faces in campaigns, sometimes without any involvement from the person being represented. One expert puts it clearly. When a public figure's face or persona is used for commercial purposes like this, it can impact their identity, reputation, and economic value, often without them knowing. A person's likeness carries meaning. It shapes how people interpret what they are seeing, whether or not the person was ever involved. Consent has to be clear, explicit, and established before any use takes place. campaignasia.com/article/when-a…

Only 0.1% of people correctly identified all of the deepfake content they were shown in controlled tests. These were not people scrolling past something quickly. They were specifically asked to evaluate what they were looking at. And still, nearly everyone missed at least some of it. That matters because this content is showing up in ads, social feeds, and news formats that people already recognize and trust. The signals people used to rely on to sense something was off, like inconsistent lighting, unnatural movement, or audio that does not quite sync, are getting harder to pick up as the technology improves. What this changes is not just how people spot fake content. It changes how they interpret everything they see, because the sense that something is real starts to become unreliable.

A lot of people assume that if they agreed to a platform's terms of service, that covers everything. But agreeing to use a platform and agreeing to have your data used to train an AI are two different things. They just get treated as the same. The way most AI systems work, if data is reachable it gets used. There is no step where anyone checks whether the person who posted it would have been okay with that specific use. Just because something is accessible does not mean the person behind it gave permission for this. That gap is bigger than most people realize. Something you posted years ago on a platform you barely use anymore could be part of a training dataset right now, and you would have no way of knowing. Our latest blog gets into how this works, what happens to data once it is in these systems, and why it is so hard to do anything about it once the process has started. identity.org/what-counts-as…