期刊
TECHNOLOGIES
卷 10, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/technologies10020046
关键词
digital cameras; forensics; face recognition; real-time systems; compressed sensing
资金
- Ford Motor Company
Face-swap-attacks pose a new threat to face recognition systems. This research proposes a noise-verification framework to address this issue, which detects alteration traces by comparing challenge images and camera enrollment photos.
Face-swap-attacks (FSAs) are a new threat to face recognition systems. FSAs are essentially imperceptible replay-attacks using an injection device and generative networks. By placing the device between the camera and computer device, attackers can present any face as desired. This is particularly potent as it also maintains liveliness features, as it is a sophisticated alternation of a real person, and as it can go undetected by traditional anti-spoofing methods. To address FSAs, this research proposes a noise-verification framework. Even the best generative networks today leave alteration traces in the photo-response noise profile; these are detected by doing a comparison of challenge images against the camera enrollment. This research also introduces compression and sub-zone analysis for efficiency. Benchmarking with open-source tampering-detection algorithms shows the proposed compressed-PRNU verification robustly verifies facial-image authenticity while being significantly faster. This demonstrates a novel efficiency for mitigating face-swap-attacks, including denial-of-service attacks.
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