Media Metadata With AIGC Identifier for Authenticity Recognition
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Solution Overview
Problem
AI-generated content (AIGC) poses challenges in authenticity and credibility, potentially spreading false information, necessitating a method to recognize its presence in media content.
Innovation Solution
A metadata generation method that includes an AIGC identifier in media content metadata, allowing users to determine authenticity and potential issues, using hash values and digital signatures to prevent tampering.
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
Solution Approach 1:
The patent applies preliminary action by embedding an AIGC identifier in the metadata during the content generation phase itself. This identifier is inserted before the content is distributed or viewed, enabling proactive identification rather than reactive detection. The metadata generation method incorporates the identifier automatically when AI generates the content, ensuring authenticity information is available from the source.
2Measurement precision
If metadata including AIGC identifier is generated for all media content, then recognition accuracy of AIGC is improved, but device complexity and processing overhead increase
Solution Approach 1:
The patent extracts the critical identification function into a separate, simple metadata field (AIGC identifier) that can be independently processed. Instead of analyzing the entire media content to determine AI generation, the system separates the identification task by using a dedicated identifier in the metadata, reducing processing complexity while maintaining recognition accuracy.
Solution Approach 2:
The metadata structure is designed to serve multiple functions: it stores the AIGC identifier for authenticity detection, and can simultaneously accommodate other media information such as copyright data, format specifications, and usage rights. This multi-functional metadata approach avoids creating separate systems for different purposes, reducing overall system complexity.
3Reliability
If hash values and digital signatures are added to metadata to prevent tampering, then data integrity and security are improved, but manufacturing complexity of metadata increases
Solution Approach 1:
The metadata generation system performs self-service by automatically generating hash values and digital signatures as part of the metadata creation process. The system that creates the metadata also generates its own integrity verification mechanisms, eliminating the need for separate external processes. This automation reduces the perceived complexity for users while maintaining security.
Data Source
AI summary
A metadata 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, whether the media content is the AIGC can be recognized based on the AIGC identifier in the metadata of the media content.