Unified Adversarial Patch for Visible-Infrared Cross-modal Attacks in the Physical World
Xingxing Wei, Yao Huang, Yitong Sun, Jie Yu

TL;DR
This paper introduces a unified adversarial patch capable of simultaneously attacking visible and infrared sensors in the physical world, using shape manipulation and robust optimization techniques to evade multi-modal object detectors.
Contribution
It proposes a novel cross-modal adversarial patch with boundary-limited shape optimization and affine-transformation enhancement for physical-world attacks on multi-sensor systems.
Findings
Achieves over 80% attack success rate against state-of-the-art detectors.
Effective in real-world scenarios with various angles, distances, and postures.
Demonstrates vulnerability of multi-modal sensor systems to unified adversarial patches.
Abstract
Physical adversarial attacks have put a severe threat to DNN-based object detectors. To enhance security, a combination of visible and infrared sensors is deployed in various scenarios, which has proven effective in disabling existing single-modal physical attacks. To further demonstrate the potential risks in such cases, we design a unified adversarial patch that can perform cross-modal physical attacks, achieving evasion in both modalities simultaneously with a single patch. Given the different imaging mechanisms of visible and infrared sensors, our work manipulates patches' shape features, which can be captured in different modalities when they undergo changes. To deal with challenges, we propose a novel boundary-limited shape optimization approach that aims to achieve compact and smooth shapes for the adversarial patch, making it easy to implement in the physical world. And a…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Bacillus and Francisella bacterial research
