GENERATIVE ADVERSARIAL NETWORKS AND DEEPFAKE FORENSICS: DETECTING SYNTHETIC AUDIO, VIDEO, AND DOCUMENT MANIPULATION

The proliferation of Generative Adversarial Networks (GANs) and related deep generative models has democratized the creation of hyper-realistic synthetic media—commonly known as deepfakes—posing unprecedented challenges to digital media authenticity, privacy, and public trust. This review provides a comprehensive and critical examination of the forensic landscape for detecting GAN-generated synthetic audio, video, and document manipulation. We systematically survey passive detection techniques that exploit artifacts inherent to the generation process, including frequency-domain anomalies, physiological inconsistencies (such as eye-blinking patterns and remote photoplethysmography signals), and Photo Response Non-Uniformity (PRNU) noise analysis. The adversarial arms race between forensic detection and anti-forensic generation is critically evaluated, highlighting how increasingly sophisticated GAN architectures continuously erode the reliability of existing detectors. We examine the legal admissibility of AI-generated forensic evidence, addressing the evidentiary challenges posed by deepfake media in civil and criminal proceedings under frameworks such as FRE 901(a). Standardized benchmarks—particularly FaceForensics++ and the Deepfake Detection Challenge (DFDC) dataset—are assessed for their role in driving reproducible research and enabling cross-method comparison. Our analysis reveals that while forensic detection methods have advanced substantially, the generalization gap across datasets and generation techniques remains a critical limitation. We identify unresolved questions regarding real-time deployment, cross-modality detection, and the integration of explainable AI for courtroom admissibility. Finally, we offer perspectives on emerging trends, including proactive fingerprinting, foundation models for forensics, and regulatory frameworks for synthetic media governance.