A Detailed Study of Robust Generative Models Adversarial Attacks and their Implications

Abstract

The need for robustness to adversarial attacks is very significant in the case of generative models, as they are employed in a variety of applications, including image synthesis, text generation, and data augmentation. In this respect, we exploit vulnerabilities in the design of generative models-Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), autoregressive models, and diffusion models. We further classify the different types of adversarial attacks, which include input attacks, latent space attacks, and model poisoning. Using broad methodologies including gradient-based approaches and optimization-based procedures, we discuss each method in detail. The causes of these kinds of attacks, from degradation of output quality to security breaches, explain why understanding and mitigating adversarial risks is essential. We introduce existing defense mechanisms, such as adversarial training, regularization techniques, and robust model architectures, and examine their effectiveness and limitations. Discussion of ongoing challenges in the conclusion includes generalization of defenses across attack types and model architectures and, as a final note, outlines various open research questions that need to be addressed for improving the resilience of generative models to adversarial threats. This work contributes to the ongoing discussion about the improvement of safety and reliability of real-world generative models.