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Synthetic media are digital artifacts (image, video, audio, text, or multimodal content) that are wholly or partly generated or materially manipulated by artificial intelligence (AI), particularly by deep-learning models. In consequence, their form, source, identity signal, or evidentiary relation to recorded events becomes partly or wholly artificial. A deepfake is the best-known subclass: AI-generated or AI-manipulated image, audio, or video content (including audio-only voice clones and still images) that realistically depicts an existing or fictitious person, object, place, or event and could falsely appear to be authentic. Contemporary scholarly and legal usage defines deepfakes by their technological origin and their capacity to deceive rather than by the creator’s intent, so disclosed and beneficial applications (accessibility, dubbing, entertainment, and research) are synthetic media as well. They are distinguished from cheapfakes (or shallowfakes), which achieve deception through conventional, non-AI editing such as selective cropping, slowing, or recontextualization. The social significance of synthetic media is not intrinsic but depends on consent, context, disclosure, and distribution, and on the institutional conditions under which audiences judge authenticity across the expanding volume of AI-generated content (AIGC) in the digital society.
- synthetic media
- deepfakes
- AI-generated content
- generative AI
- media authenticity
- content provenance
- deepfake detection
- disinformation
Synthetic media denote the class of images, sounds, videos, and texts whose production or alteration relies substantially on artificial intelligence (AI). The domain crystallized around the neologism “deepfake,” a portmanteau of “deep learning” and “fake” that emerged in late 2017 when a Reddit user, and the community named “deepfakes,” began posting AI face-swapped pornographic videos and released consumer face-swapping tools [1][2]. The term rapidly generalized from that origin to name any hyper-realistic, AI-manipulated depiction of a person, and then, as generative models matured, to sit within the broader umbrella of synthetic media alongside AI-generated content (AIGC) [1][3]. Deepfakes are best understood as the most salient subclass of synthetic media, spanning image, audio, and video, not as a separate technology [2].
The technical genealogy of the field is usually traced to the introduction of generative adversarial networks (GANs) in 2014, which established the modern paradigm of learning to generate realistic data [4]. The subsequent decade moved from research-grade face reenactment to consumer face-swap applications, and then, from about 2021, to diffusion-based text-to-image, text-to-audio, and text-to-video systems that placed high-fidelity generation in the hands of non-specialists. This diffusion of capability transformed synthetic media from a niche curiosity into an infrastructural feature of the contemporary information environment, described in legal scholarship as the arrival of a “synthetic society” in which any recorded appearance can, in principle, be fabricated or manipulated [5].
The topic matters to the digital society in two ways. Synthetic media strain the evidentiary value that audiences have historically assigned to recordings, raising the prospect that authentic material can be dismissed as fake, an effect labeled the “liar’s dividend” [6]. What synthetic media place under pressure is therefore not this or that recording but the relationship between authenticity, evidence, and public trust on which mediated communication rests; that relationship is the organizing concern of this entry. The same technologies also underpin legitimate and beneficial applications in accessibility, entertainment, education, and privacy protection, so synthetic media are better analyzed as a dual-use capability than as an intrinsic harm.
This entry approaches synthetic media from the perspective of the digital society: its defining contribution is a social-science, media-theory, and comparative-governance synthesis of what synthetic media do to authenticity, evidence, and public trust, and of how jurisdictions worldwide are responding, rather than a technical survey of generation and detection systems. On that footing, it synthesizes established scholarly and regulatory knowledge as of mid-2026, surveying competing definitions, a working typology, landmark technical milestones, detection and its documented limits, provenance and authentication, regulation and governance, societal and epistemic impacts, the media-theoretical framing of authenticity, and, closing the dual-use account, beneficial applications, before setting out open challenges. The account is deliberately bounded: it asserts only established findings, documented events, and enacted or formally proposed law, and presents genuinely contested questions as open debates. The focus accordingly falls on definitions, authenticity and evidence, human perception and trust, comparative regulation, and the media theory of the authentic; the intervening technical sections (Section 4, Section 5 and Section 6) are kept compact and reliability-oriented, and readers are pointed to related published work on the metaverse [7] and on large language models [8] rather than to re-explanations of generative-model mechanics or immersive-environment specifics.
A brief note on sources and method: as an encyclopedia entry, this text is a narrative synthesis rather than a systematic review. It rests on peer-reviewed scholarship (surveys, meta-analyses, and primary studies), on the official texts of the legal and regulatory instruments discussed, and, for developments not yet absorbed by the scholarly literature, on primary policy documents and documented incident reports, with coverage of the literature and of legal status through July 2026. Peer-reviewed and official primary sources are preferred wherever both exist; preprints, vendor announcements, and single-study or workshop-stage results are cited only with their provisional standing flagged. The same convention is applied throughout so that evidentiary weight stays visible: findings are identified in the text as experimental results, meta-analytic estimates, documented incidents, vendor-reported deployments, or early-stage demonstrations, and where authoritative sources genuinely conflict, as with the definition of the deepfake, the entry reports the disagreement rather than resolving it silently [3].
This entry is adapted from the peer-reviewed paper https://doi.org/10.3390/encyclopedia6080175
References
- Westerlund, M. The Emergence of Deepfake Technology: A Review. Technol. Innov. Manag. Rev. 2019, 9, 39–52.
- Kietzmann, J.; Lee, L.W.; McCarthy, I.P.; Kietzmann, T.C. Deepfakes: Trick or Treat? Bus. Horiz. 2020, 63, 135–146.
- Altuncu, E.; Franqueira, V.N.L.; Li, S. Deepfake: Definitions, Performance Metrics and Standards, Datasets, and a Meta-Review. Front. Big Data 2024, 7, 1400024.
- Goodfellow, I.J.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y. Generative Adversarial Nets. In Advances in Neural Information Processing Systems 27 (NeurIPS 2014); Curran Associates: Red Hook, NY, USA, 2014.
- van der Sloot, B.; Wagensveld, Y. Deepfakes: Regulatory Challenges for the Synthetic Society. Comput. Law Secur. Rev. 2022, 46, 105716.
- Chesney, R.; Citron, D.K. Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security. Calif. Law Rev. 2019, 107, 1753–1820.
- Mourtzis, D. The Metaverse in Industry 5.0: A Human-Centric Approach towards Personalized Value Creation. Encyclopedia 2023, 3, 1105–1120.
- Friedman, R. Large Language Models and Logical Reasoning. Encyclopedia 2023, 3, 687–697.
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