A Survey on Physical Adversarial Attacks against Face Recognition Systems
October 10, 2024 ยท The Cartographer ยท ๐ arXiv.org
"No code URL or promise found in abstract"
"Title-pattern auto-detect: A Survey on Physical Adversarial Attacks against Face Recognition Systems"
Evidence collected by the PWNC Scanner
Authors
Mingsi Wang, Jiachen Zhou, Tianlin Li, Guozhu Meng, Kai Chen
arXiv ID
2410.16317
Category
cs.CR: Cryptography & Security
Cross-listed
cs.AI,
cs.CV,
cs.LG
Citations
2
Venue
arXiv.org
Last Checked
4 days ago
Abstract
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Cryptography & Security
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
๐ป
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
๐ป
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
๐ป
Ghosted
How To Backdoor Federated Learning
R.I.P.
๐ป
Ghosted