Adversarial Attacks with Polarimetric Feature Constraints: A Focused Approach in Polsar Image Classification

Deep neural networks (DNNs) have been widely utilized in synthetic aperture radar (SAR) for automatic target recognition (ATR), demonstrating remarkable performance. Nevertheless, the vulnerability of DNNs to adversarial examples, particularly in SAR ATR tasks with high safety requirements, necessitates a critical examination. Existing adversarial attacks primarily concentrate on scenarios where classifier inputs consist solely of intensity information, leaving a significant research gap for attacks on classifiers that utilize polarimetric SAR (PolSAR) data. To address this gap, we propose a novel attack method aimed at PolSAR classifiers, where we manipulate the scattering matrix rather than its transformed real value images. Moreover, we incorporate a polarimetric feature constraint to enhance the stealth of the adversarial perturbations. This technique enables the generation of subtle yet effective perturbations concentrated on SAR object regions. Our experiments demonstrate high success rates in cheating state-of-the-art PolSAR classifiers and effectively evading advanced adversarial example detection methods.