  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.
