Programmable lights with secret codes could give fact-checkers a powerful tool against deepfakes, say Cornell computer scientists.
With the rise of generative AI, creating convincing deepfakes has become easier and faster than ever. These manipulated videos can spread misinformation rapidly, challenging the long-held assumption that video footage is a reliable source of truth.
. “Video used to be treated as a source of truth, but that’s no longer an assumption we can make,” said Abe Davis, assistant professor of computer science at Cornell. “Now you can pretty much create video of whatever you want. That can be fun, but also problematic, because it’s only getting harder to tell what’s real.”This trend makes it increasingly difficult to tell what is real, raising serious concerns for fact-checkers and the public alike.To tackle this challenge, a team of Cornell researchers developed a method to embed nearly invisible watermarks into videos by altering the lighting during recording. Rather than watermarking video files digitally, which requires compliance from the camera or AI model used to create the footage, this new approach hides secret codes in subtle fluctuations of light in the environment itself.Embedding Hidden Codes in LightingThe light sources, such as computer screens, photography lamps, or even ordinary room lamps equipped with a small computer chip about the size of a postage stamp, vary their brightness imperceptibly. Peter Michael, the graduate student who led the work, explained, “We used studies from human perception literature to inform our design of the coded light. The code is also designed to look like random variations that already occur in light called ‘noise,’ which also makes it difficult to detect, unless you know the secret code.” Each watermarked light source carries a unique code that can be used to verify the video later. If an adversary cuts out footage from, for example, an interview or political speech, a forensic analyst with the code can identify the missing sections. If objects are added or replaced, the altered parts typically appear black in recovered “code videos,” low-fidelity, time-stamped versions of the original scene under slightly different lighting.Verifying Authenticity with Multiple CodesThese code videos help detect when manipulated parts contradict the original lighting pattern in deepfake content. “When someone manipulates a video, the manipulated parts start to contradict what we see in these code videos, which lets us see where changes were made,” Davis said. “And if someone tries to generate fake video with AI, the resulting code videos just look like random variations.”The team has successfully used up to three separate codes for different lights in the same scene. “Even if an adversary knows the technique is being used and somehow figures out the codes, their job is still a lot harder,” Davis said. “Instead of faking the light for just one video, they have to fake each code video separately, and all those fakes have to agree with each other.”Tests show the system works in some outdoor settings and on people with different skin tones. Still, Davis warned, “This is an important ongoing problem. It’s not going to go away, and in fact, it’s only going to get harder.”
Cornell University Deepfake Detection Forensic Video Analysis Light Coding Misinformation Video Watermarking
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