Fooling Neural Networks by changing just one pixel

Deep Neural networks, being extremely effective in image classification tasks, can classify images with remarkable accuracy when trained on large enough sample. But in most cases, Deep Neural Networks are used to maximize the accuracy of a classification as a result of which the robustness of the classifier often takes a back seat. As a result of this myriad Neural Network defeating techniques have come into play. These are called adversarial attacks on a Neural Net. One important variant is known as the Fast Gradient sign method, by Ian GoodFellow et al, as seen in the paper Explaining and Harnessing Adversarial Examples.