Patch-wise Attack for Fooling Deep Neural Network

• URL: http://arxiv.org/abs/2007.06765v3
• Date: Wed, 2 Dec 2020 05:22:29 GMT
• Title: Patch-wise Attack for Fooling Deep Neural Network
• Authors: Lianli Gao and Qilong Zhang and Jingkuan Song and Xianglong Liu and Heng Tao Shen
• Abstract summary: We propose a patch-wise iterative algorithm -- a black-box attack towards mainstream normally trained and defense models. We significantly improve the success rate by 9.2% for defense models and 3.7% for normally trained models on average.
• Score: 153.59832333877543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: By adding human-imperceptible noise to clean images, the resultant adversarial examples can fool other unknown models. Features of a pixel extracted by deep neural networks (DNNs) are influenced by its surrounding regions, and different DNNs generally focus on different discriminative regions in recognition. Motivated by this, we propose a patch-wise iterative algorithm -- a black-box attack towards mainstream normally trained and defense models, which differs from the existing attack methods manipulating pixel-wise noise. In this way, without sacrificing the performance of white-box attack, our adversarial examples can have strong transferability. Specifically, we introduce an amplification factor to the step size in each iteration, and one pixel's overall gradient overflowing the $\epsilon$-constraint is properly assigned to its surrounding regions by a project kernel. Our method can be generally integrated to any gradient-based attack methods. Compared with the current state-of-the-art attacks, we significantly improve the success rate by 9.2\% for defense models and 3.7\% for normally trained models on average. Our code is available at \url{https://github.com/qilong-zhang/Patch-wise-iterative-attack}

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