Evading Real-Time Person Detectors by Adversarial T-shirt
Kaidi Xu1 Gaoyuan Zhang2 Sijia Liu2 Quanfu Fan2 Mengshu Sun1 Hongge Chen3 Pin-Yu Chen2 Yanzhi Wang1 Xue Lin1
1Northeastern University, USA 2MIT-IBM Watson AI Lab, IBM Research, USA 3Massachusetts Institute of Technology, USA
论文公开于October 25, 2019
Abstract
现有的物理对抗攻击大多集中在静态物体上,如眼镜框、停止标志和附在硬纸板上的图像。在这里,我们提出了对抗T恤,一个鲁棒的物理对抗样本用于躲避人体检测器。即使因人的姿势变换导致T恤变形,其仍具有较高的鲁棒性。我们首先对变形的影响进行建模,以设计针对t恤等非刚性物体的物理对抗实例。进一步的利用最小/最大优化,其可以同时攻击YOLOv2及Faster R-CNN.
1 Introduction
Early works studied adversarial examples in the digital space only. Recently, some works showed that it is possible to create adversarial perturbations on physical objects … However, most of the studied physical adversarial attacks encounter two limitations: a)通常考虑静态图像 b)忽略了移动目标造成的形变。 In this paper, we propose a new type of physical adversarial attack, adversarial T-shirt, to evade 引出
Most of the existing physical adversarial attacks were generated against image classifiers and object detectors. 人脸识别、stop sign、镜头贴条,EOT:旋转、平移、对比度、光照及随机噪声等。 Compared to attacki