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Attribution guided purification against adversarial patch
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AI
DOI：10.1016/j.displa.2024.102720.png)
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Abstract
En 中文
Adversarial patch is a threat to computer vision systems, as they can mislead the deep learning model by adding carefully designed stickers or patterns into images. This vulnerability has posed a serious challenge to the application of DNNs in security -critical domains such as security vision systems and autonomous driving. Researchers are actively working on defense strategies to counter adversarial patches. In this paper, a plug -andplay solution defense method is proposed that combines traditional masking with purification techniques. We implement the defense by leveraging a credible attribution algorithm mechanism. Compared to other heuristic methods, our approach minimizes the destruction of input images, reduces the distribution shift introduced by masking, and offers attack -agnostic protection without a carefully designed deep learning model. Our evaluation shows that our approach successfully enhances the security of computer vision systems against adversarial patches, safeguarding their trustworthiness in various applications.
Keywords:
Deep learning
Adversarial defense
Adversarial patch
Diffusion model