Analysing the Authentication Mechanism for GAN-Generated Adversarial Network Content through a Unified Industrial Framework: Issues, Challenges, and Future Recommendations

Generative adversarial networks (GANs) have evolved from an esoteric research area into mainstream production tools for generating images, video, audio, and text that are often indistinguishable from authentic content. As this realism grows, organizations in banking, healthcare, media, insurance, law, e-commerce, and governments have an urgent industrial problem: they need reliable methods to authenticate a given piece of digital content as being genuine or not. In this article, we examine the technical foundations of GANs, present the problem in the context of the literature on forensic-detection, provenance, and policy, review the industry-specific threats raised by synthetic media, and survey the main families of detection and authentication techniques, namely, pixel-level forensics, frequency-domain analysis, metadata inspection, deep-learning-based classifiers, watermarking, digital signatures, and provenance systems. We argue that detection is structurally insufficient due to limitations in generalizability: while detectors trained on one generative model generalize poorly to new ones, thus engendering a continuous arms race between the generator and detector, and propose a five-layer Unified Industrial Authentication Framework that combines cryptographic source verification, provenance, multi-model AI detection, integrity verification, and a formalized authentication confidence score as a viable industry solution to address this gap rather than a theoretical one. We validate our approach against known threats to validity, discuss its advantages and limitations, and conclude with industry applications, open challenges, and directions for future work.