  Deep neural networks (DNNs) are vulnerable to malicious inputs crafted by an
adversary to produce erroneous outputs. Works on securing neural networks
against adversarial examples achieve high empirical robustness on simple
datasets such as MNIST. However, these techniques are inadequate when
empirically tested on complex data sets such as CIFAR-10 and SVHN. Further,
existing techniques are designed to target specific attacks and fail to
generalize across attacks. We propose the Adversarial Model Cascades (AMC) as a
way to tackle the above inadequacies. Our approach trains a cascade of models
sequentially where each model is optimized to be robust towards a mixture of
multiple attacks. Ultimately, it yields a single model which is secure against
a wide range of attacks; namely FGSM, Elastic, Virtual Adversarial
Perturbations and Madry. On an average, AMC increases the model's empirical
robustness against various attacks simultaneously, by a significant margin (of
6.225% for MNIST, 5.075% for SVHN and 2.65% for CIFAR10). At the same time, the
model's performance on non-adversarial inputs is comparable to the
state-of-the-art models.
