Rectifying Adversarial Examples Using Their Vulnerabilities

Fumiya Morimoto, Ryuto Morita, Satoshi Ono

Deep neural network-based classifiers are prone to errors when processing adversarial examples (AEs). AEs are minimally perturbed input data undetectable to humans posing significa…