Scale-Adaptive Adversarial Patch Attack for Remote Sensing Image Aircraft Detection

Abstract

With the adversarial attack of convolutional neural networks (CNNs), we are able to generate adversarial patches to make an aircraft undetectable by object detectors instead of covering the aircraft with large camouflage nets. However, aircraft in remote sensing images (RSIs) have the problem of large variations in scale, which can easily cause size mismatches between an adversarial patch and an aircraft. A small adversarial patch has no attack effect on large aircraft, and a large adversarial patch will completely cover small aircraft so that it is impossible to judge whether the adversarial patch has an attack effect. Therefore, we propose the adversarial attack method Patch-Noobj for the problem of large-scale variation in aircraft in RSIs. Patch-Noobj adaptively scales the width and height of the adversarial patch according to the size of the attacked aircraft and generates a universal adversarial patch that can attack aircraft of different sizes. In the experiment, we use the YOLOv3 detector to verify the effectiveness of Patch-Noobj on multiple datasets. The experimental results demonstrate that our universal adversarial patches are well adapted to aircraft of different sizes on multiple datasets and effectively reduce the Average Precision (AP) of the YOLOv3 detector on the DOTA, NWPU VHR-10, and RSOD datasets by 48.2%, 23.9%, and 20.2%, respectively. Moreover, the universal adversarial patch generated on one dataset is also effective in attacking aircraft on the remaining two datasets, while the adversarial patch generated on YOLOv3 is also effective in attacking YOLOv5 and Faster R-CNN, which demonstrates the attack transferability of the adversarial patch.