FAB-Attack: Fabric-Aware Adversarial Attacks on Person Detectors under Motion Blur

Physical adversarial attacks on person detectors reveal critical vulnerabilities in safety-critical vision systems such as autonomous driving and surveillance. While recent methods enhance attack efficacy and robustness, they often neglect realistic garment deformations and motion blur, limiting real-world performance. In this work, we propose FAB-Attack (Fabric-a ware and Blur-resistant Attack), a new adversarial attack that simulates realistic garment deformation during training and targets both person detectors and image deblurring models. To enhance attack effectiveness under varying clothing deformations, we introduce a Fabric-aware Texture Appliance (FTA) module, which applies adversarial textures to clothing regions and simulates realistic fabric dynamics via physics-inspired TPS. To better emulate real-world conditions, we develop a differentiable pipeline incorporating motion blur and deblurring processes. Moreover, we demonstrate the stability of low-frequency information during motion blur's generation and removal. Based on this insight, we design a frequency band separation mechanism that suppresses high-frequency components in adversarial patterns to enhance further robustness against motion blur. Experimental results demonstrate that our approach achieves SOTA performance, reducing AP to 25.2% on the COCO dataset and achieving a 94.4% ASR in the real world under severe motion blur.