Du, M.; Sun, Y.; Sun, B.; Wu, Z.; Luo, L.; Bi, D.; Du, M. TAN: A Transferable Adversarial Network for DNN-Based UAV SAR Automatic Target Recognition Models. Drones2023, 7, 205.
Du, M.; Sun, Y.; Sun, B.; Wu, Z.; Luo, L.; Bi, D.; Du, M. TAN: A Transferable Adversarial Network for DNN-Based UAV SAR Automatic Target Recognition Models. Drones 2023, 7, 205.
Du, M.; Sun, Y.; Sun, B.; Wu, Z.; Luo, L.; Bi, D.; Du, M. TAN: A Transferable Adversarial Network for DNN-Based UAV SAR Automatic Target Recognition Models. Drones2023, 7, 205.
Du, M.; Sun, Y.; Sun, B.; Wu, Z.; Luo, L.; Bi, D.; Du, M. TAN: A Transferable Adversarial Network for DNN-Based UAV SAR Automatic Target Recognition Models. Drones 2023, 7, 205.
Abstract
In recent years, the unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) has become a highly sought-after topic for its wide applications in the field of target recognition, detection, and tracking. However, SAR automatic target recognition (ATR) models based on deep neural networks (DNN) are suffering from adversarial examples. Generally, non-cooperators rarely disclose any information about SAR-ATR models, making adversarial attacks challenging. In this situation, we propose Transferable Adversarial Network (TAN) to attack these models with highly transferable adversarial examples. The proposed method improves the transferability via a two-player game, in which we simultaneously train two encoder-decoder models: a generator that crafts malicious samples through a one-step forward mapping from original data, and an attenuator that weakens the effectiveness of malicious samples by capturing the most harmful deformations. In particular, compared to traditional iterative methods, our approach is able to one-step map original samples to adversarial examples, thus enabling real-time attacks. Experimental results indicate that the proposed approach achieves state-of-the-art transferability with acceptable adversarial perturbations and minimum time costs compared to existing attack methods, i.e., it excellently realizes real-time transferable adversarial attacks.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
