An Adaptive Adversarial Patch-Generating Algorithm for Defending against the Intelligent Low, Slow, and Small Target

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 5; p. 1439
• Main Authors: Jia, Erkenbieke, Xu, Yuelei, Zhang, Zhaoxiang, Zhang, Fan, Feng, Weijia, Dong, Liheng, Hui, Tian, Tao, Chengyang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Adaptive algorithms; adversarial patch; Algorithms; Attacking (assaulting); Classification; data collection; Datasets; Deep learning; denied environment; Drones; Embedded systems; least squares; Machine learning; Methods; Networks; Neural networks; object detection network; Object recognition; Object recognition (Computers); Optimization techniques; Pattern recognition; Remote sensing; Search methods; “low, slow, and small” target; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15051439

The “low, slow, and small” target (LSST) poses a significant threat to the military ground unit. It is hard to defend against due to its invisibility to numerous detecting devices. With the onboard deep learning-based object detection methods, the intelligent LSST (ILSST) can find and detect the ground unit autonomously in a denied environment. This paper proposes an adversarial patch-based defending method to blind the ILSST by attacking its onboard object detection network. First, an adversarial influence score was established to indicate the influence of the adversarial noise on the objects. Then, based on this score, we used the least squares algorithm and Bisectional search methods to search the patch’s optimal coordinates and size. Using the optimal coordinates and size, an adaptive patch-generating network was constructed to automatically generate patches on ground units and hide the ground units from the deep learning-based object detection network. To evaluate the efficiency of our algorithm, a new LSST view dataset was collected, and extensive attacking experiments are carried out on this dataset. The results demonstrate that our algorithm can effectively attack the object detection networks, is better than state-of-the-art adversarial patch-generating algorithms in hiding the ground units from the object detection networks, and has high transferability among the object detection networks.