SHAP-Assisted Resilience Enhancement Against Adversarial Perturbations in Optical and SAR Image Classification

The increasing reliance on convolutional neural networks (CNNs) for automatic target recognition (ATR) in critical applications necessitates robust defenses against adversarial attacks, which can undermine their reliability. To address this challenge, this letter proposes a novel classification framework that enhances CNN robustness for ATR under adversarial perturbations. Although CNNs are renowned for their high recognition accuracy, their performance can be compromised by subtle adversarial perturbations designed to deceive the classifier. Our methodology is based on extracting specific features from Shapley additive explanations (SHAP) analysis within and outside the detected target area. These features are then used to train a multinomial logistic regression model using the training labels, and the trained regressor performs the classification. The key strength of our framework relies on robustness enhancement against adversarial attacks, particularly designed by the fast gradient sign method (FGSM). We validate our findings through extensive evaluations using two publicly available datasets: the multitype aircraft remote sensing images (MTARSI) dataset, which contains optical images of various aircraft types, and the moving and stationary target acquisition and recognition (MSTAR) dataset, which contains radar images.