  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.
