Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network

Xuanqing Liu, Yao Li, Chongruo Wu, Cho-Jui Hsieh

Abstract:

We present a new algorithm to train a robust neural network against adversarial attacks.
Our algorithm is motivated by the following two ideas. First, although recent work has demonstrated that fusing randomness can improve the robustness of neural networks (Liu 2017), we noticed that adding noise blindly to all the layers is not the optimal way to incorporate randomness.
Instead, we model randomness under the framework of Bayesian Neural Network (BNN) to formally learn the posterior distribution of models in a scalable way. Second, we formulate the mini-max problem in BNN to learn the best model distribution under adversarial attacks, leading to an adversarial-trained Bayesian neural net. Experiment results demonstrate that the proposed algorithm achieves state-of-the-art performance under strong attacks. On CIFAR-10 with VGG network, our model leads to 14% accuracy improvement compared with adversarial training (Madry 2017) and random self-ensemble (Liu, 2017) under PGD attack with 0.035 distortion, and the gap becomes even larger on a subset of ImageNet.

TL;DR: We design an adversarial training method to Bayesian neural networks, showing a much stronger defense to white-box adversarial attacks

Code: [ xuanqing94/BayesianDefense](https://github.com/xuanqing94/BayesianDefense)

Data: [ImageNet](https://paperswithcode.com/dataset/imagenet), [STL-10](https://paperswithcode.com/dataset/stl-10)

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/adv-bnn-improved-adversarial-defense-through/code)