  This paper focuses on high-transferable adversarial attacks on detectors,
which are hard to attack in a black-box manner, because of their
multiple-output characteristics and the diversity across architectures. To
pursue a high attack transferability, one plausible way is to find a common
property across detectors, which facilitates the discovery of common
weaknesses. We are the first to suggest that the relevance map from
interpreters for detectors is such a property. Based on it, we design a
Relevance Attack on Detectors (RAD), which achieves a state-of-the-art
transferability, exceeding existing results by above 20%. On MS COCO, the
detection mAPs for all 8 black-box architectures are more than halved and the
segmentation mAPs are also significantly influenced. Given the great
transferability of RAD, we generate the first adversarial dataset for object
detection and instance segmentation, i.e., Adversarial Objects in COntext
(AOCO), which helps to quickly evaluate and improve the robustness of
detectors.

Computer Vision and Pattern Recognition;Cryptography and Security;Machine Learning