Analysing the Authentication Mechanism for GAN-Generated Adversarial Network Content through a Unified Industrial Framework: Issues, Challenges, and Future Recommendations | IJCSE Volume 10 – Issue 5 | IJCSE-V10I5P38
IJCSE
International Journal of Computer Science Engineering Techniques
ISSN 2455-135X · Peer-Reviewed · Open Access
📚 Volume 10, Issue 5
📅 October 16, 2026
📄 Pages 287–320
🔖 ID: IJCSE-V10I5P38
Analysing the Authentication Mechanism for GAN-Generated Adversarial Network Content through a Unified Industrial Framework: Issues, Challenges, and Future Recommendations
Author(s)
Harshita Maurya, Sakshi Prajapati, Ruchi Kumari
Abstract
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.
Keywords
Generative Adversarial Networks, deepfake detection, digital provenance, cryptographic signatures, C2PA.
Conclusion
GAN-generated content is not just a technological curiosity: it’s a trust/authentication problem with direct consequences for banking, healthcare, media, insurance, legal systems, e-commerce, and government. This article has argued that no single detection technique is likely to remain effective against the full range of present and future generative models, given the demonstrated generalization limitations of existing detectors and the ongoing arms race between generative and forensic technologies. An effective industry solution should combine cryptographic source authentication, provenance tracking, multi-model AI detection, integrity verification, and continuous monitoring, aggregated into a graded authentication confidence score rather than a binary verdict. Critically, the framework must treat an absent signature or broken hash as a signal to escalate for review (since most content in circulation today was never signed at capture at all). As generative models continue to improve, industry investment in this kind of layered, continuously updated authentication infrastructure will be essential to preserving trust in digital content
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[8] P. Dhariwal and A. Nichol, “Diffusion Models Beat GANs on Image Synthesis,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 34, 2021, pp. 8780–8794.
[9] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis with Latent Diffusion Models,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2022, pp. 10684–10695.
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[12] R. Corvi, D. Cozzolino, G. Zingarini, G. Poggi, K. Nagano, and L. Verdoliva, “On the Detection of Synthetic Images Generated by Diffusion Models,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), 2023, pp. 1–5.
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[18] A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, “FaceForensics++: Learning to Detect Manipulated Facial Images,” in Proc. IEEE/CVF Int. Conf. Computer Vision (ICCV), 2019, pp. 1–11.
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[20] R. Durall, M. Keuper, and J. Keuper, “Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2020, pp. 7887–7896.
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[27] ASIS International / Shufti, “Identity Fraud Index Report: Deepfake Identity Fraud Poised to Increase Nearly 500 Percent in 2026,” Jun. 2026.
[28] F. Marra, D. Gragnaniello, L. Verdoliva, and G. Poggi, “Do GANs Leave Artificial Fingerprints?,” in Proc. IEEE Conf. Multimedia Information Processing and Retrieval (MIPR), 2019.
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[34] J. Wondrasek, “C2PA Adoption in 2026: Hardware Platforms and Verification Reality,” softwareseni.com, Mar. 3, 2026.
[35] Apple Inc., “Secure Enclave Overview,” Apple Platform Security Guide, 2023.
[36] Coalition for Content Provenance and Authenticity (C2PA), C2PA Technical Specification, Section on Trust Models, Joint Development Foundation, 2024.
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[38] Springer Nature, “Deepfake Detection Across Image, Video, and Audio: A Comprehensive Survey with Empirical Evaluation of Generalization and Robustness,” Artificial Intelligence Review, 2026.
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[40] Liu et al., “A Review of Deepfake and Its Detection: From Generative Adversarial Networks to Diffusion Models,” International Journal of Intelligent Systems, Wiley, 2025.
[41] Zyphe, “30+ Sourced Identity Verification Statistics for 2026 (citing Sumsub, Entrust, and FBI IC3 data),” 2026.
[42] LexisNexis Risk Solutions, “Deepfake Identity Fraud Report,” Jul. 14, 2026.
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[44] R. Tolosana, R. Vera-Rodriguez, J. Fierrez, A. Morales, and J. Ortega-Garcia, “DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection,” Information Fusion, vol. 64, pp. 131–148, 2020.
[45] L. Verdoliva, “Media Forensics and DeepFakes: An Overview,” IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 5, pp. 910–932, 2020.
[46] Y. Mirsky and W. Lee, “The Creation and Detection of Deepfakes: A Survey,” ACM Computing Surveys, vol. 54, no. 1, pp. 1–41, 2021.
[47] H. Farid, “Creating, Using, Misusing, and Detecting Deep Fakes,” Journal of Online Trust and Safety, vol. 1, no. 4, 2022.
[48] National Institute of Standards and Technology (NIST), “Reducing Risks Posed by Synthetic Content: An Overview of Technical Approaches to Digital Content Transparency,” NIST AI 100-4, 2023.
[49] R. Chesney and D. Citron, “Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security,” California Law Review, vol. 107, p. 1753, 2019.
[50] D. Cozzolino, J. Thies, A. Rossler, C. Riess, M. Niessner, and L. Verdoliva, “ID-Reveal: Identity-aware DeepFake Video Detection,” in Proc. IEEE/CVF Int. Conf. Computer Vision (ICCV), 2021.
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📋 How to Cite This Paper
Harshita Maurya, Sakshi Prajapati, Ruchi Kumari (2026). Analysing the Authentication Mechanism for GAN-Generated Adversarial Network Content through a Unified Industrial Framework: Issues, Challenges, and Future Recommendations. International Journal of Computer Science Engineering Techniques, 10(5), 287–320. ISSN: 2455-135X. DOI: https://doi.org/10.5281/zenodo.23421148
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