Mixed Bayesian Stackelberg Strategies for Robust Adversarial Classifiers

Deep neural networks (DNNs) have achieved state-of-the-art performance in classification tasks; however, they are susceptible to small perturbations that are seemingly imperceptible to the human eye but are enough to fool the network into misclassifying images. To develop more robust DNNs against ad