CapGen:An Environment-Adaptive Generator of Adversarial Patches
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Abstract (arXiv)
Adversarial patches, often used to provide physical stealth protection for critical assets and assess perception algorithm robustness, usually neglect the need for visual harmony with the background environment, making them easily noticeable. Moreover, existing methods primarily concentrate on improving attack performance, disregarding the intricate dynamics of adversarial patch elements. In this work, we introduce the Camouflaged Adversarial Pattern Generator (CAPGen), a novel approach that leverages specific base colors from the surrounding environment to produce patches that seamlessly blend with their background for superior visual stealthiness while maintaining robust adversarial performance. We delve into the influence of both patterns (i.e., color-agnostic texture information) and colors on the effectiveness of attacks facilitated by patches, discovering that patterns exert a more pronounced effect on performance than colors. Based on these findings, we propose a rapid generation strategy for adversarial patches. This involves updating the colors of high-performance adversarial patches to align with those of the new environment, ensuring visual stealthiness without compromising adversarial impact. This paper is the first to comprehensively examine the roles played by patterns and colors in the context of adversarial patches.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 44 references that are in the map. First posted on arXiv 10 Dec 2024.
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References · 6
- 2018YOLOv3: An Incremental Improvementcited by 2,652
- 2016Adversarial examples in the physical worldcited by 1,762
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
- 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
- 2017Synthesizing Robust Adversarial Examplescited by 39
- 2023Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detectioncited by 5
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