Distillation-Enhanced Physical Adversarial Attacks

The study of physical adversarial patches is crucial for identifying vulnerabilities in AI-based recognition systems and developing more robust deep learning models. While recent research has focused on improving patch stealthiness for greater practical applicability, achieving an effective balance between stealth and attack performance remains a significant challenge. To address this issue, we propose a novel physical adversarial attack method that leverages knowledge distillation. Specifically, we first define a stealthy color space tailored to the target environment to ensure smooth blending. Then, we optimize an adversarial patch in an unconstrained color space, which serves as the 'teacher' patch. Finally, we use an adversarial knowledge distillation module to transfer the teacher patch's knowledge to the 'student' patch, guiding the optimization of the stealthy patch. Experimental results show that our approach improves attack performance by 20%, while maintaining stealth, highlighting its practical value.

CapGen:An Environment-Adaptive Generator of Adversarial Patches

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.

Prompt-Guided Environmentally Consistent Adversarial Patch

Adversarial attacks in the physical world pose a significant threat to the security of vision-based systems, such as facial recognition and autonomous driving. Existing adversarial patch methods primarily focus on improving attack performance, but they often produce patches that are easily detectable by humans and struggle to achieve environmental consistency, i.e., blending patches into the environment. This paper introduces a novel approach for generating adversarial patches, which addresses both the visual naturalness and environmental consistency of the patches. We propose Prompt-Guided Environmentally Consistent Adversarial Patch (PG-ECAP), a method that aligns the patch with the environment to ensure seamless integration into the environment. The approach leverages diffusion models to generate patches that are both environmental consistency and effective in evading detection. To further enhance the naturalness and consistency, we introduce two alignment losses: Prompt Alignment Loss and Latent Space Alignment Loss, ensuring that the generated patch maintains its adversarial properties while fitting naturally within its environment. Extensive experiments in both digital and physical domains demonstrate that PG-ECAP outperforms existing methods in attack success rate and environmental consistency.