Adversarially Robust Medical Classification via Attentive Convolutional Neural Networks
This addresses the critical need for robust automated medical diagnoses, though it is an incremental improvement over existing defenses.
The paper tackled the susceptibility of CNN-based medical image classifiers to adversarial examples by incorporating attention mechanisms, achieving up to 16% improvement in robust accuracy in typical scenarios and up to 2700% in extreme cases.
Convolutional neural network-based medical image classifiers have been shown to be especially susceptible to adversarial examples. Such instabilities are likely to be unacceptable in the future of automated diagnoses. Though statistical adversarial example detection methods have proven to be effective defense mechanisms, additional research is necessary that investigates the fundamental vulnerabilities of deep-learning-based systems and how best to build models that jointly maximize traditional and robust accuracy. This paper presents the inclusion of attention mechanisms in CNN-based medical image classifiers as a reliable and effective strategy for increasing robust accuracy without sacrifice. This method is able to increase robust accuracy by up to 16% in typical adversarial scenarios and up to 2700% in extreme cases.