Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation
This work addresses the problem of adversarial vulnerability in neural networks for machine learning practitioners, offering incremental improvements over existing adversarial training methods.
The paper tackles improving adversarial robustness in deep neural networks by enforcing linearity in transformed spaces and adding a Local Lipschitz regularizer, achieving state-of-the-art adversarial accuracy on MNIST, CIFAR10, and SVHN datasets.
Recent studies on the adversarial vulnerability of neural networks have shown that models trained with the objective of minimizing an upper bound on the worst-case loss over all possible adversarial perturbations improve robustness against adversarial attacks. Beside exploiting adversarial training framework, we show that by enforcing a Deep Neural Network (DNN) to be linear in transformed input and feature space improves robustness significantly. We also demonstrate that by augmenting the objective function with Local Lipschitz regularizer boost robustness of the model further. Our method outperforms most sophisticated adversarial training methods and achieves state of the art adversarial accuracy on MNIST, CIFAR10 and SVHN dataset. In this paper, we also propose a novel adversarial image generation method by leveraging Inverse Representation Learning and Linearity aspect of an adversarially trained deep neural network classifier.