Abstract

The rapid evolution of Generative Artificial Intelligence (AI) and specifically Generative Adversarial Networks (GANs) has eroded trust in digital media significantly. These technologies allow for the creation of hyper-realistic "deep fakes" including face swaps and voice cloning that pose a severe threat to digital identity verification. When weaponized on social media platforms these synthetic media become potent tools for sophisticated identity fraud, social engineering and biometric spoofing. This paper conducts a comprehensive literature review to analyze the mechanics of these threats and evaluate the efficacy of current forensic detection methodologies. The analysis reveals that existing reactive defenses suffer from critical limitations in generalizability and robustness. Standard Convolutional Neural Networks (CNNs) often fail against the compressed video formats common on social networks. Relying solely on technical detection is proving insufficient as the "arms race" between generation and detection accelerates. Consequently this study concludes that safeguarding digital ecosystems requires a holistic defense strategy. This includes the development of multi-modal detection models, the enforcement of strict legislative reforms criminalizing non-consensual deep fakes and the adoption of proactive content provenance standards.