Inter-separability and intra-concentration to enhance stochastic neural network adversarial robustness

Abstract

It has been shown that Deep Neural Networks can be easily fooled by adding an imperceptible noise termed as adversarial examples. To address this issue, in this paper, we propose a defense method called Inter-Separability and Intra-Concentration Stochastic Neural Networks (ISIC-SNN). The suggested ISIC-SNN method learns to enlarge between different label representations using label embedding and a designed inter-separability loss. It introduces uncertainty in the features latent space using the variational information bottleneck method and enhances compactness in stochastic features using intra-concentration loss. Finally, it uses dot-product similarity between stochastic feature representations and label embedding to classify features. ISIC-SNN learns in standard training which is much more efficient than adversarial training. Experiments on datasets SVHN, CIFAR-10 and CIFAR-100 demonstrate the superior defensive capability of the proposed method compared to various SNNs defensive methods.