Entropy-Based Bayesian Model Averaging for Enhancing Classification Resilience Against Adversarial Perturbations Using SAR Images

Deep learning models for Synthetic Aperture Radar (SAR) image classification are highly vulnerable to adversarial perturbations, which can significantly compromise their reliability in critical applications. In this study, we investigate the impact of adversarial attacks on SAR image classification by generating imperceptible perturbations using the Fast Gradient Sign Method (FGSM). These perturbations are designed to deceive a pre-trained convolutional neural network (CNN) into making incorrect classifications. To mitigate this vulnerability, we employ Entropy-Based Bayesian Model Averaging (EB-BMA), which is an adaptive ensemble learning approach based on model uncertainty. We fine-tune multiple CNN architectures using transfer learning from ImageNet and apply EB-BMA to dynamically adjust model contributions based on prediction confidence. This adaptive weighting mechanism ensures that models with higher certainty contribute more to the final decision, while uncertain models are down-weighted. Our results demonstrate that EB-BMA enhances resilience against adversarial perturbations, as validated on the MSTAR dataset.