AI Summary & Key Takeaways
AI InsightThe results show that no defensive layer is adequate on its own; effective mitigation of the evolving deepfake threat requires the convergence of all three.
📌 3 Key Takeaways
Abstract
Deepfake technology, driven by generative adversarial networks (GANs) and, more recently, diffusion-based models, has evolved from a technical curiosity into a serious cybersecurity threat in less than a decade. Previous studies have largely treated detection algorithms, legal regulation, and media literacy as independent lines of defence; this paper argues that any such single-layer response is structurally inadequate. Building on documented incidents — the 2022 Zelensky video, the 2019 UK CEO voice-fraud case, and the widespread circulation of non-consensual synthetic imagery — together with empirical evidence reported by Sensity AI, Sumsub, and MIT Media Lab, we develop an integrated three-layer defence framework that combines (i) technical detection, (ii) legal and regulatory instruments, and (iii) media literacy and individual verification habits. The study synthesises the current literature, maps the threat landscape across four structural dimensions — democratic integrity, institutional trust, corporate security, and individual rights — and examines implementation challenges, with particular attention to the Turkic world, where dedicated legislation remains limited. The results show that no defensive layer is adequate on its own; effective mitigation of the evolving deepfake threat requires the convergence of all three.