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Deep Neural Networks are built to generalize outside of training set in mindby using techniques such as regularization, early stopping and dropout. Butconsiderations to make them more resilient to adversarial examples are rarelytaken. As deep neural networks become more prevalent in mission-critical andreal-time systems, miscreants start to attack them by intentionally making deepneural networks to misclassify an object of one type to be seen as anothertype. This can be catastrophic in some scenarios where the classification of adeep neural network can lead to a fatal decision by a machine. In this work, weused GTSRB dataset to craft adversarial samples by Fast Gradient Sign Methodand Jacobian Saliency Method, used those crafted adversarial samples to attackanother Deep Convolutional Neural Network and built the attacked network to bemore resilient against adversarial attacks by making it more robust byDefensive Distillation and Adversarial Training

Building Robust Deep Neural Networks for Road Sign Detection