Stochastic Neural Networks (SNNs) that inject noise into their hidden layers
have recently been shown to achieve strong robustness against adversarial
attacks. However, existing SNNs are usually heuristically motivated, and often
rely on adversarial training, which is computationally costly. We propose a new
SNN that achieves state-of-the-art performance without relying on adversarial
training, and enjoys solid theoretical justification. Specifically, while
existing SNNs inject learned or hand-tuned isotropic noise, our SNN learns an
anisotropic noise distribution to optimize a learning-theoretic bound on
adversarial robustness. We evaluate our method on a number of popular
benchmarks, show that it can be applied to different architectures, and that it
provides robustness to a variety of white-box and black-box attacks, while
being simple and fast to train compared to existing alternatives.

本文提出一种新的随机神经网络，学习一种非同向噪声分布并优化学习理论上的鲁棒性，从而实现对各种白盒和黑盒攻击的强鲁棒性，相较于现有替代方案更为简单且训练速度更快。