A Survey on Physical Adversarial Attacks against Face Recognition Systems
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 105 of its 156 references that are in the map. First posted on arXiv 10 Oct 2024.
Builds on
105 of 156 references.
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Citing papers per year: 2024: 1; 2025: 0; 2026: 3.
4 papers.
- 2026Signal-based Model Access Risk Analysis for AI System Operations Security17 Jul 2026
- 20263D FaceShell: Attribute Transfer in 3D Face Avatars as a VLM Defense Mechanism9 Jul 2026
- 2026Adversarial Camouflage23 Mar 2026
- 2024Novel AI Camera Camouflage: Face Cloaking Without Full Disguise18 Dec 2024