PGD-Optimized Patch and Noise Joint Embedded Adversarial Example for Faster RCNN and YOLOv4

Faster RCNN and YOLO have stable and outstanding performance as the state of the art and widely used object detectors in real applications. However, some researches and security incidents show that Faster RCNN and YOLO can be attacked by adversarial examples, resulting in errors or invalid detection results. According to the existing attack methods on computer vision, the imperceptible perturbations embedded into the initial images can be categorized into noise disturbance and patch disturbance. In this paper, we proposed an attack method that combines the advantages of the two categories. Our attack is based on PGD iterative attacks and patch optimization. We can attack Faster RCNN and YOLOv4 successfully in less time. And the adversarial examples generated are similar to the original images, which make the attack more stealthy.