  Recent breakthroughs in the field of deep learning have led to advancements
in a broad spectrum of tasks in computer vision, audio processing, natural
language processing and other areas. In most instances where these tasks are
deployed in real-world scenarios, the models used in them have been shown to be
susceptible to adversarial attacks, making it imperative for us to address the
challenge of their adversarial robustness. Existing techniques for adversarial
robustness fall into three broad categories: defensive distillation techniques,
adversarial training techniques, and randomized or non-deterministic model
based techniques. In this paper, we propose a novel neural network paradigm
that falls under the category of randomized models for adversarial robustness,
but differs from all existing techniques under this category in that it models
each parameter of the network as a statistical distribution with learnable
parameters. We show experimentally that this framework is highly robust to a
variety of white-box and black-box adversarial attacks, while preserving the
task-specific performance of the traditional neural network model.
