Speckle-Variant Attack: Toward Transferable Adversarial Attack to SAR Target Recognition

Abstract

Recent advances of deep neural networks (DNNs) highlight the success on synthetic aperture radar automatic target recognition (SAR ATR) with superiority effectiveness and efficiency. However, the DNNs are known to be vulnerable to the 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-scaled distribution in purely digital setting. Whereas, it is not feasible to achieve this in SAR ATR tasks due to the inaccessibility of SAR system and unique imaging mechanism. In practical, 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 effective attack on major DNNs classifiers, as well as 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 reconstruct 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 through 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 on the MSTAR dataset show that the SVA has achieved the superior transferability and competitive time consumption compared with the SOTA transformation-based techniques, including the diverse inputs method and the scale-invariant method.