TAN: A Transferable Adversarial Network for DNN-Based UAV SAR Automatic Target Recognition Models

Du, Meng; Sun, Yuxin; Sun, Bing; Wu, Zilong; Li, Luo; Bi, Daping; Du, Mingyang

doi:10.3390/drones7030205

Cited by 3 publications

(1 citation statement)

References 51 publications

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“…These attacks are executed through the generation of adversarial examples, which are seemingly normal images that have been meticulously modified with imperceptible perturbations. These alterations are calculated and crafted to exploit the inherent vulnerabilities of DNNs, causing them to misinterpret the image and make incorrect decisions [10]. The process of its attack implementation is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adversarial Detection Method for UAV Vision Systems via Attribution Maps

Zhang,

Liu,

et al. 2023

Drones

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With the rapid advancement of unmanned aerial vehicles (UAVs) and the Internet of Things (IoTs), UAV-assisted IoTs has become integral in areas such as wildlife monitoring, disaster surveillance, and search and rescue operations. However, recent studies have shown that these systems are vulnerable to adversarial example attacks during data collection and transmission. These attacks subtly alter input data to trick UAV-based deep learning vision systems, significantly compromising the reliability and security of IoTs systems. Consequently, various methods have been developed to identify adversarial examples within model inputs, but they often lack accuracy against complex attacks like C&W and others. Drawing inspiration from model visualization technology, we observed that adversarial perturbations markedly alter the attribution maps of clean examples. This paper introduces a new, effective detection method for UAV vision systems that uses attribution maps created by model visualization techniques. The method differentiates between genuine and adversarial examples by extracting their unique attribution maps and then training a classifier on these maps. Validation experiments on the ImageNet dataset showed that our method achieves an average detection accuracy of 99.58%, surpassing the state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

A Novel Adversarial Detection Method for UAV Vision Systems via Attribution Maps

Zhang,

Liu,

et al. 2023

Drones

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Enhancing Adversarial Transferability With Intermediate Layer Feature Attack on Synthetic Aperture Radar Images

Wan,

Liu,

Niu

et al. 2025

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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On the adversarial robustness of aerial detection

Chen,

Chu

2024

Front. Comput. Sci.

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Deep learning-based aerial detection is an essential component in modern aircraft, providing fundamental functions such as navigation and situational awareness. Though promising, aerial detection has been shown to be vulnerable to adversarial attacks, posing significant safety concerns. The sparsity of a comprehensive analysis on the robustness of aerial detection exacerbates these vulnerabilities, increasing the risks associated with the practical application of these systems. To bridge this gap, this paper comprehensively studies the potential threats caused by adversarial attacks on aerial detection and analyzes their impact on current defenses. Based on the most widely adopted sensing strategies in aerial detection, we categorize both digital and physical adversarial attacks across optical sensing, infrared sensing, and Synthetic Aperture Radar (SAR) imaging sensing. Owing to the different imaging principles, attacks in each sensing dimension show different attack vectors and reveal varying attack potentials. Additionally, according to the operational life cycles, we analyze adversarial defenses across three operational phases: pre-mission, in-mission, and post-mission. Our findings reveal critical insights into the weaknesses of current systems and offer recommendations for future research directions. This study underscores the importance of addressing the identified challenges in adversarial attack and defense, particularly in real-world scenarios. By focusing future research on enhancing the physical robustness of detection systems, developing comprehensive defense evaluation frameworks, and leveraging high-quality platforms, we can significantly improve the robustness and reliability of aerial detection systems against adversarial threats.

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TAN: A Transferable Adversarial Network for DNN-Based UAV SAR Automatic Target Recognition Models

Cited by 3 publications

References 51 publications

A Novel Adversarial Detection Method for UAV Vision Systems via Attribution Maps

A Novel Adversarial Detection Method for UAV Vision Systems via Attribution Maps

Enhancing Adversarial Transferability With Intermediate Layer Feature Attack on Synthetic Aperture Radar Images

On the adversarial robustness of aerial detection

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