Bio-Inspired Adversarial Attack Against Deep Neural Networks

• URL: http://arxiv.org/abs/2107.02895v1
• Date: Wed, 30 Jun 2021 03:23:52 GMT
• Title: Bio-Inspired Adversarial Attack Against Deep Neural Networks
• Authors: Bowei Xi and Yujie Chen and Fan Fei and Zhan Tu and Xinyan Deng
• Abstract summary: The paper develops a new adversarial attack against deep neural networks (DNN) based on applying bio-inspired design to moving objects. To the best of our knowledge, this is the first work to introduce physical attacks with a moving object.
• Score: 28.16483200512112
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The paper develops a new adversarial attack against deep neural networks (DNN), based on applying bio-inspired design to moving physical objects. To the best of our knowledge, this is the first work to introduce physical attacks with a moving object. Instead of following the dominating attack strategy in the existing literature, i.e., to introduce minor perturbations to a digital input or a stationary physical object, we show two new successful attack strategies in this paper. We show by superimposing several patterns onto one physical object, a DNN becomes confused and picks one of the patterns to assign a class label. Our experiment with three flapping wing robots demonstrates the possibility of developing an adversarial camouflage to cause a targeted mistake by DNN. We also show certain motion can reduce the dependency among consecutive frames in a video and make an object detector "blind", i.e., not able to detect an object exists in the video. Hence in a successful physical attack against DNN, targeted motion against the system should also be considered.

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