Gradient-guided hierarchical feature attack for object detector

Abstract

Deep neural networks (DNNs) are vulnerable to adversarial attacks, which can cause security risks in computer information systems. Feature disruption attacks, as a typical form of adversarial attack, optimize adversarial examples by disrupting the intermediate features extracted by DNN. The existing feature disruption attacks have limitations when it comes to objects of different scales within resolution features, favoring low-resolution feature maps and low-scoring objects. The imbalance above affects their effectiveness in object detection tasks. Gradient-guided Hierarchical Feature Attack (GHFA) is proposed to solve these problems, which disrupts detector-extracted features using gradient-guided feature weighting. GHFA incorporates receptive field scaling and gradient scaling to balance the focus among feature maps and enhance attack performance. The evaluation of GHFA on 9 object detectors demonstrates its superior transferability compared with the 6 comparative methods, surpassing the second-ranked method by 2.4%. Furthermore, the real-world applicability of GHFA is validated available by testing it on a commercial online object detection platform.