Problem
Research questions and friction points this paper is trying to address.
trustworthy synthetic media
media provenance
visual media lifecycle
authenticity evidence
generative systems
Trustworthy synthetic visual media: Evidence across the media lifecycle
🤖 AI Summary
This study addresses the challenge of verifying the authenticity of generative media, which typically lacks a stable historical record. To overcome this limitation, this work proposes a trust framework grounded in full-lifecycle evidence that integrates media forensics, content provenance, and metadata logging techniques. The framework’s effectiveness is systematically evaluated across media processing and tampering scenarios. Rather than relying on a single universal labeling paradigm, it emphasizes the adaptability and revisability of evidence. Furthermore, this research establishes new standards for preserving the interpretability of media history, significantly enhancing the robustness of trust determinations. Ultimately, the proposed approach offers a novel methodology for the trustworthy governance of generative media.
📝 Abstract
Images and videos have long helped people understand what happened and how a work came into being. Generative systems complicate that role. Realistic media can now be produced and revised without leaving a stable history, so appearance no longer reveals whether a scene was captured, synthesized, or altered along the way. Trust must instead come from evidence that explains the path an asset has taken and the circumstances in which it was used. Some of this evidence can be recovered from the media, while some must be recorded during production and preserved as the asset circulates. This review brings those approaches together and asks when their claims remain meaningful after ordinary processing or deliberate manipulation. We argue that trustworthy media do not depend on one universal marker of authenticity. The evidence must suit the question at hand, reach the person making the judgment, and remain open to correction when better information emerges. The larger goal is to keep the history of media intelligible even as the media itself continues to change.
Innovation
Methods, ideas, or system contributions that make the work stand out.
trustworthy synthetic media
media provenance
lifecycle evidence
media forensics
generative AI
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