Deepfakes and Synthetic Media: Generation, Detection, and Governance

A new arXiv survey (2609.25017v1) concludes that effective deepfake governance requires defense-in-depth integrating forensic detection, verifiable provenance, and institutional accountability. The survey covers generation architectures including GANs, latent diffusion, neural rendering, and video synthesis, along with the spatial, temporal, frequency-domain, and physiological artifacts they produce and the CNN, transformer, and frequency-based detector families that exploit them. It identifies cross-generator generalization as the field's central open challenge and discusses cryptographic provenance standards, watermarking, and regulatory frameworks including the EU AI Act, DSA, and GDPR.

arXiv:2609.25017v1 Announce Type: new Abstract: Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains, enabling identity fraud, disinformation campaigns, and evidence fabrication. In high-stakes environments, ranging from journalism and finance to healthcare and legal contexts, the consequences extend to severe misinformation, market manipulation, identity fraud, and the erosion of institutional trust. This entry explores how modern visual intelligence and computer vision techniques are used to detect deepfakes. It outlines key deepfake generation models, such as GANs, autoencoders, neural rendering, and diffusion systems, while also explaining how adversarial methods enhance realism and challenge existing detectors. The overview highlights visual artifacts, digital patterns, and physiological cues commonly leveraged in detection and reviews major CNN, transformer, and frequency-based approaches. It also summarizes evaluation practices and the difficulty of achieving strong generalization. Finally, it identifies emerging directions, including modern intelligence techniques for civilian and military content verification. This survey covers generation architectures GANs, latent diffusion, neural rendering, video synthesis , the spatial, temporal, frequency-domain, and physiological artifacts they produce, and the detector families that exploit them. We examine evaluation benchmarks and protocols, highlighting cross-generator generalization as the field's central open challenge. Beyond detection, we discuss cryptographic provenance standards, watermarking, and regulatory frameworks EU AI Act, DSA, GDPR . We conclude that effective deepfake governance requires defense-in-depth integrating forensic detection, verifiable provenance, and institutional accountability.