Jacobian Adversarially Regularized Networks for Robustness

Understand

Adversarial examples are crafted with imperceptible perturbations with the intent to fool neural networks.

• Against such attacks, adversarial training and its variants stand as the strongest defense to date.
• Previous studies have pointed out that robust models that have undergone adversarial training tend to produce more salient and interpretable Jacobian matrices than their non-robust counterparts.
• A natural question is whether a model trained with an objective to produce salient Jacobian can result in better robustness.