Adversarial Robustness via Generative Model Defense

Abstract

This paper investigates a novel approach to enhancing the adversarial robustness of neural networks by utilizing generative models. The core idea revolves around training a generative model to accurately reconstruct the input data, thereby creating a robust representation. This reconstructed data is then used as input to the neural network, significantly reducing the impact of adversarial perturbations. We demonstrate that this defense mechanism, leveraging generative models, provides a more effective strategy compared to traditional methods. Our approach addresses a critical vulnerability in current neural network designs and offers a promising direction for building more resilient and reliable AI systems. We present a theoretical framework and outline the key components of this defense strategy. The central contribution lies in the systematic application of generative models to proactively defend against adversarial attacks, rather than reacting to them post-hoc. The results presented indicate a substantial improvement in the network's resilience to adversarial examples.