CCA: Exploring the Possibility of Contextual Camouflage Attack on Object Detection

• URL: http://arxiv.org/abs/2008.08281v1
• Date: Wed, 19 Aug 2020 06:16:10 GMT
• Title: CCA: Exploring the Possibility of Contextual Camouflage Attack on Object Detection
• Authors: Shengnan Hu, Yang Zhang, Sumit Laha, Ankit Sharma, Hassan Foroosh
• Abstract summary: We propose a contextual camouflage attack (CCA) algorithm to in-fluence the performance of object detectors. In this paper, we usean evolutionary search strategy and adversarial machine learningin interactions with a photo-realistic simulated environment. Theproposed camouflages are validated effective to most of the state-of-the-art object detectors.
• Score: 16.384831731988204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural network based object detection hasbecome the cornerstone of many real-world applications. Alongwith this success comes concerns about its vulnerability tomalicious attacks. To gain more insight into this issue, we proposea contextual camouflage attack (CCA for short) algorithm to in-fluence the performance of object detectors. In this paper, we usean evolutionary search strategy and adversarial machine learningin interactions with a photo-realistic simulated environment tofind camouflage patterns that are effective over a huge varietyof object locations, camera poses, and lighting conditions. Theproposed camouflages are validated effective to most of the state-of-the-art object detectors.

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