mlsanityData to 14 Oct 2026

Tell-Tale Watermarks for Explanatory Reasoning in Synthetic Media Forensics

arXiv 2509.05753 ↗First posted 6 Sep 2025cs.CR cs.AI cs.CVcited by 1

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Abstract (arXiv)

The rise of synthetic media has blurred the boundary between reality and fabrication under the evolving power of artificial intelligence, fueling an infodemic that erodes public trust in cyberspace. For digital imagery, a multitude of editing applications further complicates the forensic analysis, including semantic edits that alter content, photometric adjustments that recalibrate colour characteristics, and geometric projections that reshape viewpoints. Collectively, these transformations manipulate and control perceptual interpretation of digital imagery. This susceptibility calls for forensic enquiry into reconstructing the chain of events, thereby revealing deeper evidential insight into the presence or absence of criminal intent. This study seeks to address an inverse problem of tracing the underlying generation chain that gives rise to the observed synthetic media. A tell-tale watermarking system is developed for explanatory reasoning over the nature and extent of transformations across the lifecycle of synthetic media. Tell-tale watermarks are tailored to different classes of transformations, responding in a manner that is neither strictly robust nor fragile but instead interpretable. These watermarks function as reference clues that evolve under the same transformation dynamics as the carrier media, leaving interpretable traces when subjected to transformations. Explanatory reasoning is then performed to infer the most plausible account across the combinatorial parameter space of composite transformations. Experimental evaluations demonstrate the validity of tell-tale watermarking with respect to fidelity, synchronicity and traceability.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 41 of its 90 references that are in the map. First posted on arXiv 6 Sep 2025.

Builds on

41 of 90 references.

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29 more
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Citing papers per year: 2026: 1.

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  7. 2026Verifiable Provenance and Watermarking for Generative AI: An Evidentiary Framework for International Operational Law and Domestic Courts
  8. 2026Watermark Forensics for Generative Models: An Information-Theoretic Perspective