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 mechanisms that mitigate their threats. https://www.roneverhart.com/7-Plush-Oswald-Octopus/