  Neural networks are vulnerable to adversarial attacks -- small visually
imperceptible crafted noise which when added to the input drastically changes
the output. The most effective method of defending against these adversarial
attacks is to use the methodology of adversarial training. We analyze the
adversarially trained robust models to study their vulnerability against
adversarial attacks at the level of the latent layers. Our analysis reveals
that contrary to the input layer which is robust to adversarial attack, the
latent layer of these robust models are highly susceptible to adversarial
perturbations of small magnitude. Leveraging this information, we introduce a
new technique Latent Adversarial Training (LAT) which comprises of fine-tuning
the adversarially trained models to ensure the robustness at the feature
layers. We also propose Latent Attack (LA), a novel algorithm for construction
of adversarial examples. LAT results in minor improvement in test accuracy and
leads to a state-of-the-art adversarial accuracy against the universal
first-order adversarial PGD attack which is shown for the MNIST, CIFAR-10,
CIFAR-100 datasets.
