Advances in graphics and machine learning algorithms and processes 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 wide-spread distribution of false information to billions of individuals via social media platforms. One approach to thwarting the flow of fake media is to detect synthesized or modified 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 the 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—and will be increasingly relied upon. We propose AMP, a system that ensures authentication of a media content’s source 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 using the AMP service 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, allows for a consortium of media providers to govern the service while making all governance 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 an AMP manifest has been successfully located and verified.