Real-time deepfakes are no longer limited to billionaires, public figures, or those who have extensive online presences. Mittal’s research at NYU, with professors Chinmay Hegde and Nasir Memon, proposes a potential challenge-based approach to blocking AI bots from video calls, where participants would have to pass a kind of video CAPTCHA test before joining.
As Reality Defender works to improve the detection accuracy of its models, Colman says that access to more data is a critical challenge to overcome—a common refrain from the current batch of AI-focused startups. He’s hopeful more partnerships will fill in these gaps, and without specifics, hints at multiple new deals likely coming next year. After ElevenLabs was tied to a deepfake voice call of US president Joe Biden, the AI-audio startup struck a deal with Reality Defender to mitigate potential misuse.
What can you do right now to protect yourself from video call scams? Just like WIRED’s core advice about avoiding fraud from AI voice calls, not getting cocky about whether you can spot video deepfakes is critical to avoid being scammed. The technology in this space continues to evolve rapidly, and any telltale signs you rely on now to spot AI deepfakes may not be as dependable with the next upgrades to underlying models.
“We don’t ask my 80-year-old mother to flag ransomware in an email,” says Colman. “Because she’s not a computer science expert.” In the future, it’s possible real-time video authentication, if AI detection continues to improve and shows to be reliably accurate, will be as taken for granted as that malware scanner quietly humming along in the background of your email inbox.
On Wednesday, AI video synthesis firm Runway and entertainment company Lionsgate announced a partnership to create a new AI model trained on Lionsgate’s vast film and TV library. The deal will feed Runway legally clear training data and will also reportedly provide Lionsgate with tools to enhance content creation while potentially reducing production costs.
Lionsgate, known for franchises like John Wick and The Hunger Games, sees AI as a way to boost efficiency in content production. Michael Burns, Lionsgate’s vice chair, stated in a press release that AI could help develop “cutting edge, capital efficient content creation opportunities.” He added that some filmmakers have shown enthusiasm about potential applications in pre- and post-production processes.
Runway plans to develop a custom AI model using Lionsgate’s proprietary content portfolio. The model will be exclusive to Lionsgate Studios, allowing filmmakers, directors, and creative staff to augment their work. While specifics remain unclear, the partnership marks the first major collaboration between Runway and a Hollywood studio.
“We’re committed to giving artists, creators and studios the best and most powerful tools to augment their workflows and enable new ways of bringing their stories to life,” said Runway co-founder and CEO Cristóbal Valenzuela in a press release. “The history of art is the history of technology and these new models are part of our continuous efforts to build transformative mediums for artistic and creative expression; the best stories are yet to be told.”
The quest for legal training data
Generative AI models are master imitators, and video synthesis models like Runway’s latest Gen-3 Alpha are no exception. The companies that create them must amass a great deal of existing video (and still image) samples to analyze, allowing the resulting AI models to re-synthesize that information into new video generations, guided by text descriptions called prompts. And wherever that training data is lacking, it can result in unusual generations, as we saw in our hands-on evaluation of Gen-3 Alpha in July.
However, in the past, AI companies have gotten into legal trouble for scraping vast quantities of media without permission. In fact, Runway is currently the defendant in a class-action lawsuit that alleges copyright infringement for using video data obtained without permission to train its video synthesis models. While companies like OpenAI have claimed this scraping process is “fair use,” US courts have not yet definitively ruled on the practice. With other potential legal challenges ahead, it makes sense from Runway’s perspective to reach out and sign deals for training data that is completely in the clear.
Even if the training data becomes fully legal and licensed, different elements of the entertainment industry view generative AI on a spectrum that seems to range between fascination and horror. The technology’s ability to rapidly create images and video based on prompts may attract studios looking to streamline production. However, it raises polarizing concerns among unions about job security, actors and musicians about likeness misuse and ethics, and studios about legal implications.
So far, news of the deal has not been received kindly among vocal AI critics found on social media. On X, filmmaker and AI critic Joe Russo wrote, “I don’t think I’ve ever seen a grosser string of words than: ‘to develop cutting-edge, capital-efficient content creation opportunities.'”
Film concept artist Reid Southen shared a similar negative take on X: “I wonder how the directors and actors of their films feel about having their work fed into the AI to make a proprietary model. As an artist on The Hunger Games? I’m pissed. This is the first step in trying to replace artists and filmmakers.”
It’s a fear that we will likely hear more about in the future as AI video synthesis technology grows more capable—and potentially becomes adopted as a standard filmmaking tool. As studios explore AI applications despite legal uncertainties and labor concerns, partnerships like the Lionsgate-Runway deal may shape the future of content creation in Hollywood.
Enlarge/ Snapshots from three videos generated using OpenAI’s Sora.
On Thursday, OpenAI announced Sora, a text-to-video AI model that can generate 60-second-long photorealistic HD video from written descriptions. While it’s only a research preview that we have not tested, it reportedly creates synthetic video (but not audio yet) at a fidelity and consistency greater than any text-to-video model available at the moment. It’s also freaking people out.
“It was nice knowing you all. Please tell your grandchildren about my videos and the lengths we went to to actually record them,” wrote Wall Street Journal tech reporter Joanna Stern on X.
“This could be the ‘holy shit’ moment of AI,” wrote Tom Warren of The Verge.
“Every single one of these videos is AI-generated, and if this doesn’t concern you at least a little bit, nothing will,” tweeted YouTube tech journalist Marques Brownlee.
For future reference—since this type of panic will some day appear ridiculous—there’s a generation of people who grew up believing that photorealistic video must be created by cameras. When video was faked (say, for Hollywood films), it took a lot of time, money, and effort to do so, and the results weren’t perfect. That gave people a baseline level of comfort that what they were seeing remotely was likely to be true, or at least representative of some kind of underlying truth. Even when the kid jumped over the lava, there was at least a kid and a room.
The prompt that generated the video above: “A movie trailer featuring the adventures of the 30 year old space man wearing a red wool knitted motorcycle helmet, blue sky, salt desert, cinematic style, shot on 35mm film, vivid colors.“
Technology like Sora pulls the rug out from under that kind of media frame of reference. Very soon, every photorealistic video you see online could be 100 percent false in every way. Moreover, every historical video you see could also be false. How we confront that as a society and work around it while maintaining trust in remote communications is far beyond the scope of this article, but I tried my hand at offering some solutions back in 2020, when all of the tech we’re seeing now seemed like a distant fantasy to most people.
In that piece, I called the moment that truth and fiction in media become indistinguishable the “cultural singularity.” It appears that OpenAI is on track to bring that prediction to pass a bit sooner than we expected.
Prompt: Reflections in the window of a train traveling through the Tokyo suburbs.
OpenAI has found that, like other AI models that use the transformer architecture, Sora scales with available compute. Given far more powerful computers behind the scenes, AI video fidelity could improve considerably over time. In other words, this is the “worst” AI-generated video is ever going to look. There’s no synchronized sound yet, but that might be solved in future models.
How (we think) they pulled it off
AI video synthesis has progressed by leaps and bounds over the past two years. We first covered text-to-video models in September 2022 with Meta’s Make-A-Video. A month later, Google showed off Imagen Video. And just 11 months ago, an AI-generated version of Will Smith eating spaghetti went viral. In May of last year, what was previously considered to be the front-runner in the text-to-video space, Runway Gen-2, helped craft a fake beer commercial full of twisted monstrosities, generated in two-second increments. In earlier video-generation models, people pop in and out of reality with ease, limbs flow together like pasta, and physics doesn’t seem to matter.
Sora (which means “sky” in Japanese) appears to be something altogether different. It’s high-resolution (1920×1080), can generate video with temporal consistency (maintaining the same subject over time) that lasts up to 60 seconds, and appears to follow text prompts with a great deal of fidelity. So, how did OpenAI pull it off?
OpenAI doesn’t usually share insider technical details with the press, so we’re left to speculate based on theories from experts and information given to the public.
OpenAI says that Sora is a diffusion model, much like DALL-E 3 and Stable Diffusion. It generates a video by starting off with noise and “gradually transforms it by removing the noise over many steps,” the company explains. It “recognizes” objects and concepts listed in the written prompt and pulls them out of the noise, so to speak, until a coherent series of video frames emerge.
Sora is capable of generating videos all at once from a text prompt, extending existing videos, or generating videos from still images. It achieves temporal consistency by giving the model “foresight” of many frames at once, as OpenAI calls it, solving the problem of ensuring a generated subject remains the same even if it falls out of view temporarily.
OpenAI represents video as collections of smaller groups of data called “patches,” which the company says are similar to tokens (fragments of a word) in GPT-4. “By unifying how we represent data, we can train diffusion transformers on a wider range of visual data than was possible before, spanning different durations, resolutions, and aspect ratios,” the company writes.
An important tool in OpenAI’s bag of tricks is that its use of AI models is compounding. Earlier models are helping to create more complex ones. Sora follows prompts well because, like DALL-E 3, it utilizes synthetic captions that describe scenes in the training data generated by another AI model like GPT-4V. And the company is not stopping here. “Sora serves as a foundation for models that can understand and simulate the real world,” OpenAI writes, “a capability we believe will be an important milestone for achieving AGI.”
One question on many people’s minds is what data OpenAI used to train Sora. OpenAI has not revealed its dataset, but based on what people are seeing in the results, it’s possible OpenAI is using synthetic video data generated in a video game engine in addition to sources of real video (say, scraped from YouTube or licensed from stock video libraries). Nvidia’s Dr. Jim Fan, who is a specialist in training AI with synthetic data, wrote on X, “I won’t be surprised if Sora is trained on lots of synthetic data using Unreal Engine 5. It has to be!” Until confirmed by OpenAI, however, that’s just speculation.
