Security Issues in Generative Adversarial Networks
Atul B. Kathole, Kapil Netaji Vhatkar, Roshani Raut, Sonali Chandrashekhar Patil, Anuja R. Jadhav · 2023
Due to its generative model’s persuasive capacity to create realistic examples plausibly derived from an existing distribution of samples, generative adversarial networks (GANs) have encouraged various applications in computer vision and natural language processing, among others. Not only does GAN perform well on data creation challenges, but its game-theoretic optimization approach also promotes fertilization for privacy and security-related research. Unfortunately, there are no thorough studies on GAN in privacy and security, and this study will summarize them methodically. Security and its associated charge are an iterative pair of objects that evolve in response to one another’s advancements—a cybersecurity “arms race.” The purpose of this study is to examine the many ways in which GANs have been utilized to offer both security advancements and attack scenarios to circumvent detection systems. This study aims to look at recent work in GANs, particularly in device and network security. Additionally, this chapter addresses new difficulties for intrusion detection systems derived from GANs. As well as discuss several possible GAN privacy and security applications and discusses some future research possibilities.