Rectifying Adversarial Examples Using Their Vulnerabilities

Deep neural network-based classifiers are prone to errors when processing adversarial examples AEs. AEs are minimally perturbed input data undetectable to humans posing significant risks to security-dependent applications. Hence, extensive research has been undertaken to develop defense mechanism...