  Adversarial attacks have demonstrated the vulnerability of Machine Learning
(ML) image classifiers in Synthetic Aperture Radar (SAR) Automatic Target
Recognition (ATR) systems. An adversarial attack can deceive the classifier
into making incorrect predictions by perturbing the input SAR images, for
example, with a few scatterers attached to the on-ground objects. Therefore, it
is critical to develop robust SAR ATR systems that can detect potential
adversarial attacks by leveraging the inherent uncertainty in ML classifiers,
thereby effectively alerting human decision-makers. In this paper, we propose a
novel uncertainty-aware SAR ATR for detecting adversarial attacks.
Specifically, we leverage the capability of Bayesian Neural Networks (BNNs) in
performing image classification with quantified epistemic uncertainty to
measure the confidence for each input SAR image. By evaluating the uncertainty,
our method alerts when the input SAR image is likely to be adversarially
generated. Simultaneously, we also generate visual explanations that reveal the
specific regions in the SAR image where the adversarial scatterers are likely
to to be present, thus aiding human decision-making with hints of evidence of
adversarial attacks. Experiments on the MSTAR dataset demonstrate that our
approach can identify over 80% adversarial SAR images with fewer than 20% false
alarms, and our visual explanations can identify up to over 90% of scatterers
in an adversarial SAR image.
