Universal Physical Adversarial Attack via Background Image

Recently, adversarial attacks against object detectors have become research hotspots in academia. However, digital adversarial attacks need to generate adversarial perturbation on digital images in a “pixel-wise” way, which is challenging to deploy accurately in the real world. Physical adversarial attacks usually need to paste the adversarial patches on the surface of target objects one by one, which is not suitable for objects with complex shapes and is challenging to deploy in practice. In this paper, we propose a universal background adversarial attack method for deep learning object detection, which puts the target objects on the universal background image and changes the local pixel information around the target objects so that the object detectors cannot recognize the target objects. This method takes the form of a universal background image for the physical adversarial attack and is easy to deploy in the real world. It can use a single universal background image to attack different classes of target objects simultaneously and has good robustness under different angles and distances. Extensive experiments have shown that the universal background attack can successfully attack two object detection models, YOLO v3 and Faster R-CNN, with average success rates of 74.9% and 67.8% with varying distances from 15 cm to 60 cm and angels from - 90 ^∘ to 90 ^∘ in the physical world.