Balancing Realism and Attack Efficacy: Adversarial Texture Generation From Authentic Clothing Patterns

Abstract

Adversarial attacks against object detectors have often been limited by the use of visually conspicuous or synthetic patterns, reducing their practicality in real-world scenarios. In this work, a novel framework is proposed to generate naturalistic adversarial textures by transforming authentic Southeast Asian clothing designs through a structured pipeline. The method is built upon 2D-to-3D UV mapping, dual K-means clustering for pattern simplification, and differentiable Voronoi-based optimization to enable gradient-based attacks while preserving visual realism. The effectiveness of the approach is evaluated using dynamic human models rendered with 360掳 viewpoint rotations and varying body geometries based on the SMPL framework. Detection confidence from YOLOv3 is consistently suppressed, with an average value of 0.3966 observed across five body models. Strong generalization is demonstrated under diverse poses and viewpoints, confirming the robustness of the adversarial textures. These findings suggest that visually plausible garments can be exploited to achieve stealthy adversarial effects, and future work may explore extensions to handle challenging lighting conditions and real-time applications.

Lab Members

Patrick Chan

Associate Professor, Vice Dean

Patrick Chan works on machine learning, deep learning, image processing, adversarial learning, and secure machine learning.