Object Hider: Adversarial Patch Attack Against Object Detectors
This addresses security vulnerabilities in object detection systems, which are critical for computer vision applications, but is incremental as it builds on existing adversarial attack methods.
The paper tackles adversarial attacks on object detectors by proposing two adversarial patch generation algorithms, achieving high effectiveness and transferability, and placing top 7 in a competition with 1701 teams.
Deep neural networks have been widely used in many computer vision tasks. However, it is proved that they are susceptible to small, imperceptible perturbations added to the input. Inputs with elaborately designed perturbations that can fool deep learning models are called adversarial examples, and they have drawn great concerns about the safety of deep neural networks. Object detection algorithms are designed to locate and classify objects in images or videos and they are the core of many computer vision tasks, which have great research value and wide applications. In this paper, we focus on adversarial attack on some state-of-the-art object detection models. As a practical alternative, we use adversarial patches for the attack. Two adversarial patch generation algorithms have been proposed: the heatmap-based algorithm and the consensus-based algorithm. The experiment results have shown that the proposed methods are highly effective, transferable and generic. Additionally, we have applied the proposed methods to competition "Adversarial Challenge on Object Detection" that is organized by Alibaba on the Tianchi platform and won top 7 in 1701 teams. Code is available at: https://github.com/FenHua/DetDak