Second Order Optimization for Adversarial Robustness and Interpretability
This addresses the problem of efficient adversarial robustness training for deep learning practitioners, offering a more scalable alternative to AT while maintaining interpretability.
The paper tackles the high computational cost of Adversarial Training (AT) for deep neural networks by proposing a novel regularizer that uses first and second order information to approximate adversarial loss, achieving stronger robustness than prior methods with significantly lower training time and producing human-interpretable features for model explanations.
Deep neural networks are easily fooled by small perturbations known as adversarial attacks. Adversarial Training (AT) is a technique aimed at learning features robust to such attacks and is widely regarded as a very effective defense. However, the computational cost of such training can be prohibitive as the network size and input dimensions grow. Inspired by the relationship between robustness and curvature, we propose a novel regularizer which incorporates first and second order information via a quadratic approximation to the adversarial loss. The worst case quadratic loss is approximated via an iterative scheme. It is shown that using only a single iteration in our regularizer achieves stronger robustness than prior gradient and curvature regularization schemes, avoids gradient obfuscation, and, with additional iterations, achieves strong robustness with significantly lower training time than AT. Further, it retains the interesting facet of AT that networks learn features which are well-aligned with human perception. We demonstrate experimentally that our method produces higher quality human-interpretable features than other geometric regularization techniques. These robust features are then used to provide human-friendly explanations to model predictions.