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