  Despite their tremendous success in modelling high-dimensional data
manifolds, deep neural networks suffer from the threat of adversarial attacks -
Existence of perceptually valid input-like samples obtained through careful
perturbation that lead to degradation in the performance of the underlying
model. Major concerns with existing defense mechanisms include
non-generalizability across different attacks, models and large inference time.
In this paper, we propose a generalized defense mechanism capitalizing on the
expressive power of regularized latent space based generative models. We design
an adversarial filter, devoid of access to classifier and adversaries, which
makes it usable in tandem with any classifier. The basic idea is to learn a
Lipschitz constrained mapping from the data manifold, incorporating adversarial
perturbations, to a quantized latent space and re-map it to the true data
manifold. Specifically, we simultaneously auto-encode the data manifold and its
perturbations implicitly through the perturbations of the regularized and
quantized generative latent space, realized using variational inference. We
demonstrate the efficacy of the proposed formulation in providing resilience
against multiple attack types (black and white box) and methods, while being
almost real-time. Our experiments show that the proposed method surpasses the
state-of-the-art techniques in several cases.
