LogicGAN–based Data Augmentation Approach to Improve Adversarial Attack DNN Classifiers

This paper presents an innovative algorithmic approach in order to improve adversarial attack classifiers, based on data augmented by minor modifications generated by a logicGAN. Therefore, the paper addresses a particular type of mitigation against adversarial attacks, which consists of training the "attacked" classifier with initial and adversarial data already known by the defender. Accordingly, we propose an algorithm that improves the training of the classifier: (1) by generating complementary adversarial data which instead of coming from the known adversarial attack, comes directly from minor modifications resulting from the already known adversarial data, and (2) by generating these minor modifications using a specific kind of generative adversarial network named logicGAN. By using an xAI system, this derivative of GAN has the particularity of yielding more substantial corrective feedback from the discriminator to the generator and, thereby, making the mitigation of adversarial attacks faster.