An Universal Perturbation Generator for Black-Box Attacks Against Object Detectors

Abstract

With the continuous development of deep neural networks (DNNs), it has become the main means of solving problems in the field of computer vision. However, recent research has shown that deep neural networks are vulnerable to well-designed adversarial examples. In this paper, we used a deep neural network to generate adversarial examples to attack black-box object detectors. We trained a generation network to produce universal perturbations, achieving a cross-task attack against black-box object detectors. We demonstrated the feasibility of task-generalizable attacks. Our attack generated efficient universal perturbations on classifiers then attack object detectors. We proved the effectiveness of our attack on two representative object detectors: Faster R-CNN based on proposal and regression-based YOLOv3.