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A Survey on Physical Adversarial Attacks against Face Recognition Systems

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arxiv 2410.16317 v1 pith:4H2QQ4Y5 submitted 2024-10-10 cs.CR cs.AIcs.CVcs.LG

classification cs.CRcs.AIcs.CVcs.LG
keywords physicalattackssystemsadversarialresearchchallengesdirectionsface
verification ladder T0 review T1 audit T2 compute T3 formal
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As Face Recognition (FR) technology becomes increasingly prevalent in finance, the military, public safety, and everyday life, security concerns have grown substantially. Physical adversarial attacks targeting FR systems in real-world settings have attracted considerable research interest due to their practicality and the severe threats they pose. However, a systematic overview focused on physical adversarial attacks against FR systems is still lacking, hindering an in-depth exploration of the challenges and future directions in this field. In this paper, we bridge this gap by comprehensively collecting and analyzing physical adversarial attack methods targeting FR systems. Specifically, we first investigate the key challenges of physical attacks on FR systems. We then categorize existing physical attacks into three categories based on the physical medium used and summarize how the research in each category has evolved to address these challenges. Furthermore, we review current defense strategies and discuss potential future research directions. Our goal is to provide a fresh, comprehensive, and deep understanding of physical adversarial attacks against FR systems, thereby inspiring relevant research in this area.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Novel AI Camera Camouflage: Face Cloaking Without Full Disguise

    cs.CV 2024-12 reject novelty 2.0 of 10

    Subtle cosmetic lines near facial key points and an alpha-layer PNG trick are claimed to hide faces from commercial detectors, but the evidence is anecdotal and not reproducible.

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