In an era where manipulated videos can spread disinformation, bully people, and incite harm, UC Riverside researchers have created a powerful new system to expose these fakes.
Amit Roy-Chowdhury, a professor of electrical and computer engineering, and doctoral candidate Rohit Kundu, both from UCR's Marlan and Rosemary Bourns College of Engineering, teamed up with Google scientists to develop an artificial intelligence model that detects video tampering -- even when manipulations go far beyond face swaps and altered speech. (Roy-Chowdhury is also the co-director of the UC Riverside Artificial Intelligence Research and Education (RAISE) Institute, a new interdisciplinary research center at UCR.)
Their new system, called the Universal Network for Identifying Tampered and synthEtic videos (UNITE), detects forgeries by examining not just faces but full video frames, including backgrounds and motion patterns. This analysis makes it one of the first tools capable of identifying synthetic or doctored videos that do not rely on facial content.
"Deepfakes have evolved," Kundu said. "They're not just about face swaps anymore. People are now creating entirely fake videos -- from faces to backgrounds -- using powerful generative models. Our system is built to catch all of that."
UNITE's development comes as text-to-video and image-to-video generation have become widely available online. These AI platforms enable virtually anyone to fabricate highly convincing videos, posing serious risks to individuals, institutions, and democracy itself.
"It's scary how accessible these tools have become," Kundu said. "Anyone with moderate skills can bypass safety filters and generate realistic videos of public figures saying things they never said."
Kundu explained that earlier deepfake detectors focused almost entirely on face cues.
"If there's no face in the frame, many detectors simply don't work," he said. "But disinformation can come in many forms. Altering a scene's background can distort the truth just as easily."
To address this, UNITE uses a transformer-based deep learning model to analyze video clips. It detects subtle spatial and temporal inconsistencies -- cues often missed by previous systems. The model draws on a foundational AI framework known as SigLIP, which extracts features not bound to a specific person or object. A novel training method, dubbed "attention-diversity loss," prompts the system to monitor multiple visual regions in each frame, preventing it from focusing solely on faces.
The result is a universal detector capable of flagging a range of forgeries -- from simple facial swaps to complex, fully synthetic videos generated without any real footage.
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