Optimally configured generative adversarial networks to distinguish real and AI-generated human faces

Artificial Intelligence (AI) has come a long way in the last several years, especially in terms of producing human-like faces with deep-fake technology. However, the challenge lies in accurately distinguishing between real and AI-generated human faces. As the applications of such technology continue to expand, the need for robust classification methods becomes crucial to ensure ethical and responsible use. Existing Generative Adversarial Networks (GANs) produce increasingly realistic synthetic faces, making it difficult for traditional methods to differentiate between real and generated faces. This poses potential risks in various domains, including security, identity verification, and misinformation. The primary objective of this research is to design an optimally configured GAN capable of distinguishing between real and generated faces and to develop a robust classifier that accurately classifies human faces as either real or generative. The results showcase the effectiveness of the optimally configured GAN model in achieving high accuracy, reaching 95%, in distinguishing between real and AI-generated faces across state-of-the-art techniques. The research contributes to the ethical deployment of AI technologies, safeguards security applications, strengthens identity verification systems, combats misinformation, and fosters public trust in the era of advanced AI.