Universal adversarial perturbations against object detection

Highlights：

• highlights

• We propose an algorithm to generate universal adversarial perturbations against object detection. To the best of our knowledge, this work is the first one that empirically proves the existence of such perturbations, which can lead the target detector to fail in finding any objects on most images.

• We introduce two criteria to evaluate the blind degree of the detectors and show that detectors are highly vulnerable to such universal perturbations.

• We analyze the generalization of the universal perturbations across different training sets, backbone networks, and detectors, which shows promising universality in such black-box attack settings.

• We also use the proposed method to generate the class-specific universal perturbations, which can remove the detection results of the target class and keep the results of other classes unchanged.