Generating Adversarial yet Inconspicuous Patches with a Single Image

• URL: http://arxiv.org/abs/2009.09774v2
• Date: Wed, 21 Apr 2021 12:05:48 GMT
• Title: Generating Adversarial yet Inconspicuous Patches with a Single Image
• Authors: Jinqi Luo, Tao Bai, Jun Zhao
• Abstract summary: We propose an approach to gen-erate adversarial yet inconspicuous patches with onesingle image. In our approach, adversarial patches areproduced in a coarse-to-fine way with multiple scalesof generators and discriminators. Our ap-proach shows strong attacking ability in both the white-box and black-box setting.
• Score: 15.217367754000913
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks have been shown vulnerable toadversarial patches, where exotic patterns can resultin models wrong prediction. Nevertheless, existing ap-proaches to adversarial patch generation hardly con-sider the contextual consistency between patches andthe image background, causing such patches to be eas-ily detected and adversarial attacks to fail. On the otherhand, these methods require a large amount of data fortraining, which is computationally expensive. To over-come these challenges, we propose an approach to gen-erate adversarial yet inconspicuous patches with onesingle image. In our approach, adversarial patches areproduced in a coarse-to-fine way with multiple scalesof generators and discriminators. Contextual informa-tion is encoded during the Min-Max training to makepatches consistent with surroundings. The selection ofpatch location is based on the perceptual sensitivity ofvictim models. Through extensive experiments, our ap-proach shows strong attacking ability in both the white-box and black-box setting. Experiments on saliency de-tection and user evaluation indicate that our adversar-ial patches can evade human observations, demonstratethe inconspicuousness of our approach. Lastly, we showthat our approach preserves the attack ability in thephysical world.

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