In this paper, we presented systematic solutions to build robust and
practical AEs against real world object detectors. Particularly, for Hiding
Attack (HA), we proposed the feature-interference reinforcement (FIR) method
and the enhanced realistic constraints generation (ERG) to enhance robustness,
and for Appearing Attack (AA), we proposed the nested-AE, which combines two
AEs together to attack object detectors in both long and short distance. We
also designed diverse styles of AEs to make AA more surreptitious. Evaluation
results show that our AEs can attack the state-of-the-art real-time object
detectors (i.e., YOLO V3 and faster-RCNN) at the success rate up to 92.4% with
varying distance from 1m to 25m and angles from -60{\deg} to 60{\deg}. Our AEs
are also demonstrated to be highly transferable, capable of attacking another
three state-of-the-art black-box models with high success rate.

本研究提出了一种针对实时目标检测器的建设鲁棒且实用的 AE 系统解决方案，包括针对 HA 的 FIR 方法和 ERG 增强鲁棒性，以及针对 AA 的嵌套 AE，可攻击不同距离和角度下的检测器。实验表明，我们的 AE 系统的攻击成功率可以达到 92.4％，并且可攻击另外三种黑盒模型。