The fundamental challenge in SAR target detection lies in developing discriminative, efficient, and robust representations of target characteristics within intricate non-cooperative environments. However, accurate target detection is impeded by factors including the sparse distribution and discrete features of the targets, as well as complex background interference. In this study, we propose a Gamma Diffusion Model Network with MambaSAR module (MaDiNet) for SAR target detection. Specifically, MaDiNet leverages the Gamma distribution to model the statistical characteristics of SAR images, and conceptulizes SAR target detection as the task of generating target bounding boxes in the image space. Furthermore, we design a MambaSAR module to capture intricate spatial structural information of targets and enhance the capability of the model to differentiate between targets and complex backgrounds. The experimental results on multi-class target detection datasets have all achieved SOTA, with a particularly notable improvement of 6.7% in mAP50 on the ODSOG-1.0 dataset, proving the effectiveness of the proposed network. Code is available at https://github.com/JoyeZLearning/MaDiNet.

Deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target recognition (ATR) systems have been shown to be highly vulnerable to adversarial perturbations that are deliberately designed yet almost imperceptible but can bias DNN inference when added to targeted objects. This leads to serious safety concerns when applying DNNs to high-stakes SAR ATR applications. Therefore, enhancing the adversarial robustness of DNNs is essential for applying DNNs to modern real-world SAR ATR systems. Toward building more robust DNN-based SAR ATR models, this article explores the domain knowledge of the SAR imaging process and proposes a novel scattering model guided adversarial attack (SMGAA) algorithm, which can generate adversarial perturbations in the form of electromagnetic scattering response (called adversarial scatterers). The proposed SMGAA consists of two parts: 1) a parametric scattering model and corresponding imaging method and 2) a customized gradient-based optimization algorithm. First, we introduce the effective attributed scattering center model (ASCM) and a general imaging method to describe the scattering behavior of typical geometric structures in the SAR imaging process. By further devising several strategies to take the domain knowledge of SAR target images into account and relax the greedy search procedure, the proposed method does not need to be prudentially fine-tuned and can efficiently find the effective ASCM parameters to fool the SAR classifiers and facilitate the robust model training. Comprehensive evaluations on the moving and stationary target acquisition and recognition (MSTAR) dataset show that the adversarial scatterers generated by SMGAA are more robust to perturbations and transformations in the SAR processing chain than the currently studied attacks and are effective to construct a defensive model against the malicious scatterers.

Recent advances in deep neural networks (DNNs) highlight the success of synthetic aperture radar automatic target recognition (SAR ATR) with superior effectiveness and efficiency. However, the DNNs are known to be vulnerable to adversarial examples, whose performance will be dramatically reduced when the imperceptible perturbation exists. In optical image processing, invisible perturbations are typically embedded in the way of a full-scale distribution in a purely digital setting. Whereas, it is not feasible to achieve this in SAR ATR tasks due to the inaccessibility of the SAR system and unique imaging mechanism. In practice, the subtle perturbations could be produced by physical approaches that change the scattering property of the target. Therefore, the adversarial perturbations for SAR ATR should be of good transferability to achieve an effective attack on major DNN classifiers as well as the accessible additive region in SAR images with respect to the realistic target locations. In this letter, we present a novel approach, namely speckle-variant attack (SVA). The proposed SVA is composed of two major modules: an iterative gradient-based perturbation generator and a target region extractor. The perturbation generator implements a speckle-variant transformation that continuously reconstructs the speckle-noise pattern during each of the iterations for strong transferability. The target region extractor ensures the feasibility of the additive adversarial perturbations in practical scenarios by restricting the region of the perturbation. Therefore, the proposed SVA is capable of producing adversarial examples that are more transferable and physically feasible. Extensive evaluations of the MSTAR dataset show that the SVA has achieved superior transferability and competitive time consumption compared with the SOTA transformation-based techniques, including the diverse inputs method and the scale-invariant method.

The absence of publicly available, large-scale, high-quality datasets for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has significantly hindered the application of rapidly advancing deep learning techniques, which hold huge potential to unlock new capabilities in this field. This is primarily because collecting large volumes of diverse target samples from SAR images is prohibitively expensive, largely due to privacy concerns, the characteristics of microwave radar imagery perception, and the need for specialized expertise in data annotation. Throughout the history of SAR ATR research, there have been only a number of small datasets, mainly including targets like ships, airplanes, buildings, etc. There is only one vehicle dataset MSTAR collected in the 1990s, which has been a valuable source for SAR ATR. To fill this gap, this paper introduces a large-scale, new dataset named ATRNet-STAR with 40 different vehicle categories collected under various realistic imaging conditions and scenes. It marks a substantial advancement in dataset scale and diversity, comprising over 190,000 well-annotated samples—$10\times$ larger than its predecessor, the famous MSTAR. Building such a large dataset is a challenging task, and the data collection scheme will be detailed. Second, we illustrate the value of ATRNet-STAR via extensively evaluating the performance of 15 representative methods with 7 different experimental settings on challenging classification and detection benchmarks derived from the dataset. Finally, based on our extensive experiments, we identify valuable insights for SAR ATR and discuss potential future research directions in this field. We hope that the scale, diversity, and benchmark of ATRNet-STAR can significantly facilitate the advancement of SAR ATR.

It has been demonstrated that deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target recognition (ATR) techniques are extremely susceptible to adversarial intrusions, that is, malicious SAR images including deliberately generated perturbations that are imperceptible to the human eye but can deflect DNN inference. Attack algorithms in previous studies are based on direct access to a ATR model such as gradients or training data to generate adversarial examples for a target SAR image, which is against the non-cooperative nature of ATR applications. In this article, we establish a fully black-box universal attack (FBUA) framework to craft one single universal adversarial perturbation (UAP) against a wide range of DNN architectures as well as a large fraction of target images. It is of both high practical relevance for an attacker and a risk for ATR systems that the UAP can be designed by an FBUA in advance and without any access to the victim DNN. The proposed FBUA can be decomposed to three main phases: (1) SAR images simulation, (2) substitute model training, and (3) UAP generation. Comprehensive evaluations on the MSTAR and SARSIM datasets demonstrate the efficacy of the FBUA, i.e., can achieve an average fooling ratio of 64.6% on eight cutting-edge DNNs (when the magnitude of the UAP is set to 16/255). Furthermore, we empirically find that the black-box UAP mainly functions by activating spurious features which can effectively couple with clean features to force the ATR models to concentrate on several categories and exhibit a class-wise vulnerability. The proposed FBUA aligns with the non-cooperative nature and reveals the access-free adversarial vulnerability of DNN-based SAR ATR techniques, providing a foundation for future defense against black-box threats.