Implicit adversarial data augmentation and robustness with Noise-based Learning. (September 2021)

• Record Type: Journal Article
• Authors: Panda, Priyadarshini; Roy, Kaushik
• Is Part Of: Neural networks. Volume 141(2021)
• Page Start: 120
• Page End: 132
• Publication Date: 2021-09
• Subjects: Adversarial robustness -- Deep learning -- Principal Component Analysis; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2021.04.008
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: We introduce a Noise-based Learning (NoL) approach for training neural networks that are intrinsically robust to adversarial attacks. We find that the learning of random noise introduced with the input with the same loss function used during posterior maximization, improves a model's adversarial resistance. We show that the learnt noise performs implicit adversarial data augmentation boosting a model's adversary generalization capability. We evaluate our approach's efficacy and provide a simplistic visualization tool for understanding adversarial data, using Principal Component Analysis. We conduct comprehensive experiments on prevailing benchmarks such as MNIST, CIFAR10, CIFAR100, Tiny ImageNet and show that our approach performs remarkably well against a wide range of attacks. Furthermore, combining NoL with state-of-the-art defense mechanisms, such as adversarial training, consistently outperforms prior techniques in both white-box and black-box attacks.