论文标题
CCA：探索上下文伪装攻击对象检测的可能性
CCA: Exploring the Possibility of Contextual Camouflage Attack on Object Detection
论文作者
论文摘要
基于神经网络的深度对象检测Hasbecome是许多现实世界应用的基石。这项成功也引起了人们对其脆弱性的态度攻击的关注。为了获得对这个问题的更多了解，我们提出上下文伪装攻击（简称CCA）算法，以体内对象检测器的性能。在本文中，我们介绍了与光真实的模拟环境互动的进化搜索策略和对抗机的学习互动，这些环境在巨大的物体位置，相机姿势和照明条件下有效地有效。对大多数最先进的对象探测器有效验证了伪装。
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.
