{"id":634311,"date":"2020-01-31T18:53:40","date_gmt":"2020-02-01T02:53:40","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&#038;p=634311"},"modified":"2024-04-24T03:26:33","modified_gmt":"2024-04-24T10:26:33","slug":"amp-authentication-of-media-via-provenance","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/amp-authentication-of-media-via-provenance\/","title":{"rendered":"AMP &#8211; Authentication of Media via Provenance"},"content":{"rendered":"<section class=\"mb-3 moray-highlight\">\n\t<div class=\"card-img-overlay mx-lg-0\">\n\t\t<div class=\"card-background bg-gray-200 has-background- card-background--full-bleed\">\n\t\t\t\t\t<\/div>\n\t\t<!-- Foreground -->\n\t\t<div class=\"card-foreground d-flex mt-md-n5 my-lg-5 px-g px-lg-0\">\n\t\t\t<!-- Container -->\n\t\t\t<div class=\"container d-flex mt-md-n5 my-lg-5 \">\n\t\t\t\t<!-- Card wrapper -->\n\t\t\t\t<div class=\"w-100 w-lg-col-5\">\n\t\t\t\t\t<!-- Card -->\n\t\t\t\t\t<div class=\"card material-md-card py-5 px-md-5\">\n\t\t\t\t\t\t<div class=\"card-body \">\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n<h1 class=\"wp-block-heading h2\" id=\"amp-authentication-of-media-via-provenance\">AMP &#8211; Authentication of Media via Provenance<\/h1>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n<p>Advances in graphics and machine learning algorithms have led to the general availability of easy-to-use tools for modifying and synthesizing media. The proliferation of these tools threatens democracies around the world by enabling widespread distribution of false information to billions of individuals via social media platforms, and by casting doubt on the veracity of all media. One approach to thwarting the flow of fake media is to detect modified or synthesized media via the use of pattern recognition methods, including statistical classifiers developed via machine learning. 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. Within a short period of time, 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\u2014 and will be increasingly relied upon. We propose AMP, a system that ensures authentication of a media content\u2019s provider via provenance. AMP creates one or more 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, also 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 an icon or other visual elements in the browser, indicating that a AMP manifest has been successfully located and verified.<\/p>\n\n\n","protected":false},"excerpt":{"rendered":"<p>Advances in graphics and machine learning algorithms have led to the general availability of easy-to-use tools for modifying and synthesizing media. The proliferation of these tools threatens democracies around the world by enabling widespread distribution of false information to billions of individuals via social media platforms, and by casting doubt on the veracity of all [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13547],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-634311","msr-project","type-msr-project","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us","msr-archive-status-complete"],"msr_project_start":"","related-publications":[805828,634320,712411,723907],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Rico Malvar","user_id":32786,"people_section":"Section name 0","alias":"malvar"},{"type":"user_nicename","display_name":"Eric Horvitz","user_id":32033,"people_section":"Section name 0","alias":"horvitz"},{"type":"user_nicename","display_name":"Amaury Chamayou","user_id":38362,"people_section":"Section name 0","alias":"amchamay"},{"type":"user_nicename","display_name":"Sylvan Clebsch","user_id":36368,"people_section":"Section name 0","alias":"syclebsc"},{"type":"user_nicename","display_name":"Manuel Costa","user_id":32794,"people_section":"Section name 0","alias":"manuelc"},{"type":"guest","display_name":"Shabnam Erfani","user_id":632865,"people_section":"Section name 0","alias":""},{"type":"user_nicename","display_name":"C\u00e9dric Fournet","user_id":31819,"people_section":"Section name 0","alias":"fournet"},{"type":"user_nicename","display_name":"Kevin Kane","user_id":32554,"people_section":"Section name 0","alias":"kkane"},{"type":"user_nicename","display_name":"Jay Stokes","user_id":32427,"people_section":"Section name 0","alias":"jstokes"}],"msr_research_lab":[199561,199565],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/634311"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":6,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/634311\/revisions"}],"predecessor-version":[{"id":1027500,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/634311\/revisions\/1027500"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=634311"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=634311"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=634311"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=634311"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=634311"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}