AIGC Metadata Identification for Media Authenticity Verification
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Solution Overview
Problem
The authenticity and credibility of AI-generated content (AIGC) are often compromised, leading to potential spread of false information, and existing methods lack effective means to recognize and manage such content.
Innovation Solution
A metadata generation method that includes an AIGC identifier in media content metadata, utilizing hash values and digital signatures to ensure authenticity and allow for recognition of AIGC, along with additional information like model details and usage permissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI-generated content is created to meet user preferences and scenarios, then content diversity and user satisfaction are improved, but authenticity and credibility of information deteriorate due to potential false information spread
Solution Approach 1:
The patent introduces metadata as an intermediary element that carries authentication information between the AIGC generation system and users. The metadata includes digital watermarks, hash values, and authentication tokens that serve as mediators to verify the origin and authenticity of AI-generated content without affecting the content diversity itself.
Solution Approach 2:
The patent implements preliminary authentication by embedding metadata with digital watermarks and hash values during the AIGC generation process itself. This preliminary action ensures that authenticity verification capabilities are built into the content before it is distributed, preventing false information spread while maintaining content versatility.
2Measurement precision
If metadata with AIGC identifiers is added to media content, then recognition capability of AIGC is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent extracts the authentication verification function into a separate metadata processing layer. Instead of complicating the main media content processing, the system extracts authentication information into standalone metadata fields that can be processed independently using simple hash comparisons and digital watermark detection, thereby reducing overall system complexity while maintaining high recognition accuracy.
3Reliability
If comprehensive metadata including hash values and digital signatures is implemented, then content authentication capability is improved, but data processing time increases
Solution Approach 1:
The patent performs preliminary computation of hash values and digital signatures during the AIGC generation phase itself. By calculating these authentication elements in advance and embedding them in metadata before content distribution, the system avoids time-consuming authentication computations during content usage and verification, thus reducing real-time processing delays while maintaining strong authentication capabilities.
Data Source
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AI summary
Embodiments of this application provide a metadata generation method, a recognition method, and an electronic device. The method includes: first, obtaining media content; and then, generating metadata of the media content, where the metadata of the media content includes an artificial intelligence generated content AIGC identifier, and the AIGC identifier indicates whether the media content is AIGC. Further, subsequently, whether the media content is the AIGC can be recognized based on the AIGC identifier in the metadata of the media content. In this way, it can help a user determine authenticity of the media content to some extent, and improve vigilance of the user in using (for example, forwarding) the media content to some extent.