Dual Watermark Embedding for Model-Independent Source Tracing
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Solution Overview
Problem
Existing watermarking solutions for content generated by machine learning models face challenges such as increased computational costs, overfitting risks, and complex operations, and require accessing the generation model for extraction, leading to inefficiencies and risks of watermark failure.
Innovation Solution
Embed dual watermarks into generated content based on user and model identifiers without modifying the model, using a first watermark for user source and a second watermark for model source, enabling extraction without model access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If watermark embedding is performed using existing solutions for machine learning generated content, then watermark robustness is improved, but computational cost increases and operational complexity increases
Solution Approach 1:
The patent segments the watermark embedding process into two independent components: a first watermark embedding module that operates independently of the generation model, and a second watermark embedding module that integrates with the model. This segmentation allows the first watermark to be embedded without modifying the model structure, thereby reducing operational complexity while maintaining robustness through dual watermark protection
Solution Approach 2:
The patent applies preliminary action by embedding the first watermark into the generated content before the content leaves the generation system, using only the generated content and a first identifier. This preliminary watermarking step is performed without requiring access to the generation model's internal parameters or structure, simplifying the operational process while ensuring watermark robustness is established early in the content lifecycle
2Reliability
If watermark embedding requires accessing the generation model for extraction, then watermark security is improved, but extraction efficiency deteriorates and model access risks increase
Solution Approach 1:
The patent extracts the watermark extraction process from the generation model itself, creating an independent extraction mechanism. The first watermark can be extracted using only the generated content and first identifier, without requiring access to the generation model's internal structure or parameters. This extraction independence significantly improves efficiency by eliminating model access requirements while maintaining security through the use of identifiers that are not publicly accessible
Solution Approach 2:
The patent introduces identifiers (first identifier and second identifier) as intermediaries that bridge the watermark embedding and extraction processes. These identifiers serve as mediators that allow watermark extraction without direct model access, thereby improving extraction efficiency while maintaining security through the use of authenticated identifier verification
3Loss of information
If dual watermarks are embedded into generated content, then content source traceability is improved, but embedding complexity increases
Solution Approach 1:
The patent segments the dual watermark embedding into two independent embedding operations with different complexity characteristics. The first watermark embedding uses only the generated content and first identifier without requiring model access, while the second watermark embedding integrates with the generation model. This segmentation allows the simpler first watermark process to handle basic traceability requirements, reducing overall embedding complexity while maintaining comprehensive source traceability through the combination of both watermarks
Solution Approach 2:
The patent applies local quality by assigning different embedding strategies to different watermark components based on their specific requirements. The first watermark uses a simplified embedding process suitable for its identification purpose, while the second watermark uses a more sophisticated process integrated with the generation model for enhanced traceability. This differentiated approach optimizes the balance between traceability quality and embedding complexity for each watermark component
Data Source
AI summary
A method in an illustrative embodiment includes generating, by a content generation model, first content based on a user-inputted instruction. The method further includes generating a first watermark for determining a user source based on a user identifier. The method further includes generating a second watermark for determining a model source based on a model parameter of the content generation model. The method further includes embedding the first watermark and the second watermark into the first content to generate second content with dual watermarks. In this way, a content source can be traced without modifying or retraining the content generation model, thereby simplifying the watermark embedding process, and improving the watermark embedding efficiency. In addition, the watermark can be extracted without accessing the content generation model, so that the watermark extraction process is more convenient and efficient.