A Review of Tools and Technologies to Combat Deepfakes

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information-17-00347.pdf

published 113334 2 342171 document 113334 lightbox.jpg image/png 87c3588f364672b18972d547cdc803ec MD5 50115 2026-04-03 09:28:30 https://pure.iiasa.ac.at/id/eprint/21428/2/lightbox.jpg 21428 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/113333 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/113333 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/113333 archive 2 disk0/00/02/14/28 2026-04-03 09:28:29 2026-04-03 09:28:29 2026-04-03 09:28:29 article show Erokhin D. 9200 0000-0002-5191-0579 Komendantova N. 1880 0000-0003-2568-6179 A Review of Tools and Technologies to Combat Deepfakes pub ASA CAT deepfakes; synthetic media; multimedia forensics; deepfake detection; provenance; authentication; content credentials; watermarking; adversarial machine learning; evaluation metrics Deepfakes and adjacent synthetic-media capabilities have become a systemic challenge for information integrity, security, and digital trust. Countermeasures now span passive detection methods that infer manipulation from content traces, active provenance systems that cryptographically bind metadata to media, and watermarking approaches that embed detectable signals into content or generative processes. This review presents a rigorous synthesis of tools and technologies to combat deepfakes across modalities (image, video, audio, and selected multimodal settings), drawing primarily from the peer-reviewed literature, standardized benchmarks, and official technical specifications and reports. The review analyzes detection methods, provenance and authentication technologies, with emphasis on cryptographic manifests and threat models, watermarking and content provenance, including diffusion-era watermarking and industrial deployments, adversarial robustness and attacker adaptation, datasets and benchmarks, evaluation metrics across tasks, and deployment and scalability constraints. A dedicated section addresses legal, ethical, and policy issues, focusing on emerging transparency obligations and platform governance. The review finds that no single countermeasure is sufficient in realistic adversarial settings. The strongest practical approach is a layered defense that combines provenance, watermarking, content-based detection, and human oversight. The study concludes with limitations of the current evidence base and prioritized research directions to improve generalization, interoperability, and trustworthy user experiences. 2026-04-03 MDPI 10.3390/info17040347 3134 161 public Information 17 e347 TRUE 2078-2489 info:eu-repo/semantics/article