Kan AI opdage deepfake-videoer ved at analysere mikroskopiske uoverensstemmelser i blinkemønstre ?

AI-forskere har opdaget, at syntetiske videoer konsekvent viser unaturlige øjenblink-dynamikker. Disse systemer anvender højopløselig videoudanalyse til at identificere uoverensstemmelser, der er usynlige for det menneskelige øje. Teknikken virker på tværs af de fleste nuværende deepfake-genereringsmetoder. Dog udvikles der allerede nye adversariale angreb for at omgå sådan detektion.

Background

Current deepfake detection methods do analyze subtle physiological cues, and blinking patterns have been explored because synthesized faces often produce unnaturally consistent or infrequent blinks. Research shows that deep neural networks can learn to detect these microscopic inconsistencies by examining blink frequency, duration, and eyelid motion dynamics, sometimes achieving high accuracy on controlled datasets (Li, Y., et al. "Exposing AI-Generated Faces by Detecting Eye Blinking Anomalies." 2022 IEEE International Conference on Multimedia and Expo (ICME)). However, as generative models improve, attackers can refine blinking behavior to evade such detectors, making this approach increasingly unreliable as a standalone defense. Performance varies widely across lighting conditions, head poses, and video compression, limiting real-world applicability. New adversarial attacks are already being developed to bypass such detection.