We describe a method to produce a network where current methods such as
DeepFool have great difficulty producing adversarial samples. Our construction
suggests some insights into how deep networks work. We provid
MagNet is proposed as a defense mechanism for neural network classifiers against adversarial examples in deep learning, which learns to differentiate between normal and adversarial examples by approximating the manifold of normal examples and reconstructing adversarial examples by moving them towards the manifold, and it also proposes a mechanism to defend against graybox attack by using diversity to strengthen MagNet.