AMP: Authentication of Media via Provenance
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Abstract (arXiv)
Advances in graphics and machine learning have led to the general availability of easy-to-use tools for modifying and synthesizing media. The proliferation of these tools threatens to cast doubt on the veracity of all media. One approach to thwarting the flow of fake media is to detect modified or synthesized media through machine learning methods. While detection may help in the short term, we believe that it is destined to fail as the quality of fake media generation continues to improve. Soon, neither humans nor algorithms will be able to reliably distinguish fake versus real content. Thus, pipelines for assuring the source and integrity of media will be required---and increasingly relied upon. We propose AMP, a system that ensures the authentication of media via certifying provenance. AMP creates one or more publisher-signed manifests for a media instance uploaded by a content provider. These manifests are stored in a database allowing fast lookup from applications such as browsers. For reference, the manifests are also registered and signed by a permissioned ledger, implemented using the Confidential Consortium Framework (CCF). CCF employs both software and hardware techniques to ensure the integrity and transparency of all registered manifests. AMP, through its use of CCF, enables a consortium of media providers to govern the service while making all its operations auditable. The authenticity of the media can be communicated to the user via visual elements in the browser, indicating that an AMP manifest has been successfully located and verified.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jan 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2; 2026: 3.
6 papers.
- 2022On the Horizon: Interactive and Compositional Deepfakescited by 13
- 2025Atlas: A Framework for ML Lifecycle Provenance & Transparencycited by 8
- 2025Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individualscited by 2
- 2026Soft Redaction of Image Provenance via Zero-Knowledge Proofscited by 0
- 2026Signpost Watermarking: Joint Optimization for Visual Watermark Coexistencecited by 0
- 2026A Locally Tokenized Generative Model for Robust Time-Series Watermarkingcited by 0
- 2026A Locally Tokenized Generative Model for Robust Time-Series Watermarking20 Aug 2026
- 2026Signpost Watermarking: Joint Optimization for Visual Watermark Coexistence10 Aug 2026
- 2026Soft Redaction of Image Provenance via Zero-Knowledge Proofs7 Aug 2026
- 2025Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individuals21 May 2025
- 2025Atlas: A Framework for ML Lifecycle Provenance & Transparency26 Feb 2025
- 2022On the Horizon: Interactive and Compositional Deepfakes5 Sep 2022