New research from New York University adds to the growing indications that we may soon have to take the deepfake equivalent of a'drunk test' in order to authenticate ourselves, before commencing a sensitive video call – such as a work-related videoconference, or any other sensitive scenario that may attract fraudsters using real-time deepfake streaming software. Some of the active and passive challenges applied to video-call scenarios in GOTCHA. The user must comply with and pass the challenges, while additional'passive' methods (such as attempting to overload a potential deepfake system) are used over which the participant has no influence. The proposed system is titled GOTCHA – a tribute to the CAPTCHA systems that have become an increasing obstacle to web-browsing over the last 10-15 years, wherein automated systems require the user to perform tasks that machines are bad at, such as identifying animals or deciphering garbled text (and, ironically, these challenges often turn the user into a free AMT-style outsourced annotator). In essence, GOTCHA extends the August 2022 DF-Captcha paper from Ben-Gurion University, which was the first to propose making the person at the other end of the call jump through a few visually semantic hoops in order to prove their authenticity.

A new collaboration between a researcher from the United States' National Security Agency (NSA) and the University of California at Berkeley offers a novel method for detecting deepfake content in a live video context – by observing the effect of monitor lighting on the appearance of the person at the other end of the video call. Popular DeepFaceLive user Druuzil Tech & Games tries out his own Christian Bale DeepFaceLab model in a live session with his followers, while lighting sources change. The system works by placing a graphic element on the user's screen that changes a narrow range of its color faster than a typical deepfake system can respond – even if, like real-time deepfake streaming implementation DeepFaceLive (pictured above), it has some capability of maintaining live color transfer, and accounting for ambient lighting. The uniform color image displayed on the monitor of the person at the other end (i.e. the potential deepfake fraudster) cycles through a limited variation of hue-changes that are designed not to activate a webcam's automatic white balance and other ad hoc illumination compensation systems, which would compromise the method. From the paper, an illustration of change in lighting conditions from the monitor in front of a user, which effectively operates as a diffuse'area light'.