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.
翻译:图像和机器学习的进步导致普遍提供易于使用的修改和综合媒体的工具。这些工具的扩散有可能使人对所有媒体的真实性产生怀疑。阻止假媒体流动的一种方法是通过机器学习方法检测经修改或合成的媒体。虽然检测在短期内可能有所帮助,但我们认为,随着假媒体生成的质量不断提高,这些清单注定会失败。很快,无论是人还是算法都无法可靠地区分假媒体和真实内容。因此,保证媒体来源和完整性的管道将越来越需要并日益依赖。我们提议AMP,这是一个通过认证认证确保媒体认证的系统。AMP为内容提供者上传的媒体提供一种或多种出版商签名的演示品。这些演示品储存在一个数据库中,允许通过浏览器等应用程序快速查看。关于参考,清单也由使用机密联合框架(CCF)实施的允许的分类簿登记和签名。CCF使用软件和硬件技术确保媒体来源和完整性,通过认证证明证明文件的认证认证认证程序认证媒体的认证。AMP创建了一个或多个出版商签名的媒体清单清单,同时通过浏览器将所有已注册的客户端端端端端端端端的系统进行验证。AMP的验证。AMP可以使用所有客户端端端端端查。