Invisible Mask: Practical Attacks on Face Recognition with Infrared
Zhe Zhou, Di Tang, Xiaofeng Wang, Weili Han, Xiangyu Liu, Kehuan Zhang

TL;DR
This paper introduces a novel infrared-based adversarial attack on face recognition systems, demonstrating high success rates and highlighting a new security threat that is invisible to the naked eye.
Contribution
We propose the first infrared adversarial attack method against face recognition, capable of bypassing or misleading systems without observable cues.
Findings
Over 70% success rate in generating infrared adversarial examples
Infrared attacks can impersonate victims and bypass surveillance
Attacks are unobservable to humans and require small, discreet devices
Abstract
Accurate face recognition techniques make a series of critical applications possible: policemen could employ it to retrieve criminals' faces from surveillance video streams; cross boarder travelers could pass a face authentication inspection line without the involvement of officers. Nonetheless, when public security heavily relies on such intelligent systems, the designers should deliberately consider the emerging attacks aiming at misleading those systems employing face recognition. We propose a kind of brand new attack against face recognition systems, which is realized by illuminating the subject using infrared according to the adversarial examples worked out by our algorithm, thus face recognition systems can be bypassed or misled while simultaneously the infrared perturbations cannot be observed by raw eyes. Through launching this kind of attack, an attacker not only can dodge…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Face recognition and analysis · Anomaly Detection Techniques and Applications
