Title: Spatially-Aware U-Net to Defend against Adversarial Attacks on YOLO Object Detectors
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Object Detection, Adversarial Attacks, Defense, YOLO

Abstract: Object detection models play a key role in self-driving systems where real-time high accuracy performance is crucial. While deep neural networks are performant, they are highly vulnerable to adversarial attacks which are subtle perturbations in the input image that induce detection errors. However, there are limited works on improving robustness of object detectors. While several diffusion-based methods have achieved strong defense against adversarial attacks, they often require a lot of computing resources and do not offer real-time inference speeds. This paper explores a defense mechanism to enhance the robustness of YOLO-based object detection systems against adversarial attacks in autonomous driving scenarios without modifying the underlying detector. The proposed defense is an attention U-Net augmented with spatial attention layers to enable the network to capture the salient information despite adversarial perturbations from the input. The training of our defender is guided by the defended detector to not only improve robustness against adversarial examples but also maintain performance when evaluating benign images. Benchmarking against previous defense approaches, our method achieves thorough protection with less damage to the original detection ability. Moreover, our defense model exhibits a generalized defensive ability against various unseen adversarial attacks.