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Deep neural network based object detection hasbecome the cornerstone of manyreal-world applications. Alongwith this success comes concerns about itsvulnerability tomalicious attacks. To gain more insight into this issue, weproposea contextual camouflage attack (CCA for short) algorithm to in-fluencethe performance of object detectors. In this paper, we usean evolutionarysearch strategy and adversarial machine learningin interactions with aphoto-realistic simulated environment tofind camouflage patterns that areeffective over a huge varietyof object locations, camera poses, and lightingconditions. Theproposed camouflages are validated effective to most of thestate-of-the-art object detectors.

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