Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation
Junbin Fang, Canjian Jiang, You Jiang, Puxi Lin, Zhaojie Chen, Yujing, Sun, Siu-Ming Yiu, Zoe L. Jiang

TL;DR
This paper introduces a novel, practical physical adversarial attack on face recognition systems using LED illumination modulation, exploiting the rolling shutter effect to generate imperceptible luminance changes that fool detection and verification models with high success rates.
Contribution
The authors propose a new low-cost, inconspicuous physical attack method leveraging LED modulation and rolling shutter effect, addressing limitations of digital and existing physical attacks.
Findings
Achieves up to 100% success rate in dodging face verification.
Reaches over 97% success in denial-of-service attacks on face detection.
Demonstrates effectiveness against multiple face recognition models.
Abstract
Although face recognition starts to play an important role in our daily life, we need to pay attention that data-driven face recognition vision systems are vulnerable to adversarial attacks. However, the current two categories of adversarial attacks, namely digital attacks and physical attacks both have drawbacks, with the former ones impractical and the latter one conspicuous, high-computational and inexecutable. To address the issues, we propose a practical, executable, inconspicuous and low computational adversarial attack based on LED illumination modulation. To fool the systems, the proposed attack generates imperceptible luminance changes to human eyes through fast intensity modulation of scene LED illumination and uses the rolling shutter effect of CMOS image sensors in face recognition systems to implant luminance information perturbation to the captured face images. In…
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Taxonomy
TopicsFace recognition and analysis · Adversarial Robustness in Machine Learning · Biometric Identification and Security
MethodsAdditive Angular Margin Loss
