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arXiv · 2001.07886

AMP: Authentication of Media via Provenance

Paul England·Henrique S. Malvar·Eric Horvitz·Jack W. Stokes·Cédric Fournet·Rebecca Burke-Aguero·Amaury Chamayou·Sylvan Clebsch·Manuel Costa·John Deutscher·Shabnam Erfani·Matt Gaylor·Andrew Jenks·Kevin Kane·Elissa Redmiles·Alex Shamis·Isha Sharma·Sam Wenker·Anika Zaman
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Abstract

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.

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Paul England, Henrique S. Malvar, Eric Horvitz, Jack W. Stokes, Cédric Fournet, Rebecca Burke-Aguero, Amaury Chamayou, Sylvan Clebsch, Manuel Costa, John Deutscher, Shabnam Erfani, Matt Gaylor, Andrew Jenks, Kevin Kane, Elissa Redmiles, Alex Shamis, Isha Sharma, Sam Wenker, Anika Zaman. 2020-01-22. AMP: Authentication of Media via Provenance. https://arxiv.org/abs/2001.07886

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