  Advancing defensive mechanisms against adversarial attacks in generative
models is a critical research topic in machine learning. Our study focuses on a
specific type of generative models - Variational Auto-Encoders (VAEs). Contrary
to common beliefs and existing literature which suggest that noise injection
towards training data can make models more robust, our preliminary experiments
revealed that naive usage of noise augmentation technique did not substantially
improve VAE robustness. In fact, it even degraded the quality of learned
representations, making VAEs more susceptible to adversarial perturbations.
This paper introduces a novel framework that enhances robustness by
regularizing the latent space divergence between original and noise-augmented
data. Through incorporating a paired probabilistic prior into the standard
variational lower bound, our method significantly boosts defense against
adversarial attacks. Our empirical evaluations demonstrate that this approach,
termed Robust Augmented Variational Auto-ENcoder (RAVEN), yields superior
performance in resisting adversarial inputs on widely-recognized benchmark
datasets.
