SentinelAdvMedical: toward adversarial attacks detection on medical image classification via out-of-distribution strategies

Deep Learning (DL) comprehends methods to enhance medical image classification and help physicians speed up diagnosis. However, these methods present security issues and are vulnerable to adversarial attacks that result in the model’s misclassification, presenting severe consequences in the medical field. We propose SentinelAdvMedical, a novel pipeline to detect adversarial attacks by employing controlled Out-of-Distributions (OOD) strategies to enhance the “immunity” of DL models. Towards that end, we studied the classification of Optical Coherence Tomography (OCT) images of Skin lesions with ResNet50. Our findings show that the best OOD detectors for OCT and Skin Lesion datasets are MaxLogits and Entropy, which outperform baselines Maximum Softmax Probabilities (MSP) and Mahalanobis feature-based score. To conduct this study, we developed a novel pipeline and studied the application of OOD strategies against adversarial examples, aiming to detect them and provide security specialists with a path to check possible attacked spots in medical datasets employing the best OOD detectors in these settings.