Explainable Deep Generative Models with Adversarial Sample Defense

Abstract

Deep generative models have achieved remarkable success in various domains, including image generation, text generation, and music generation. However, these models often operate as black boxes, lacking transparency and interpretability. Furthermore, they are highly vulnerable to adversarial attacks, where subtle perturbations in the input can lead to drastically different and incorrect outputs. This research addresses these critical limitations by proposing a novel framework that integrates explainable AI (XAI) techniques with adversarial training methods. We leverage SHAP (SHapley Additive exPlanations) values to provide insights into the generative model's decision-making process, enabling us to understand the factors driving the generated outputs. Simultaneously, we employ adversarial training to enhance the model's robustness against adversarial attacks. Our approach aims to create a deep generative model that is both interpretable and resilient to malicious inputs. The key contributions of this work are the combined approach of XAI-driven interpretation and adversarial defense, offering a pathway towards more trustworthy and secure deep generative models.