Latent-Aware Image Watermarking During Generative Model Decoding
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Solution Overview
Problem
Existing image watermarking methods for AI-generated content are inefficient, requiring post-generation processing, leading to trade-offs between watermarked image quality, extraction accuracy, and robustness, and lack effective methods for model ownership verification.
Innovation Solution
An in-generation watermarking method that integrates watermarking into the generative model's decoding process, using multi-layer decoder models with machine-learned parameters to embed imperceptible watermarks, ensuring robustness and high capacity without degrading image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If post-generation watermarking is applied, then watermark extraction accuracy is improved, but image quality deteriorates and processing time increases
Solution Approach 1:
The patent embeds watermarks during the image generation process itself, before the image is completed. The watermark is integrated into the latent space representations during diffusion or generation steps, eliminating the need for separate post-generation watermarking operations and reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent combines the watermarking operation with the image generation operation into a single unified process. The watermark embedding and image synthesis occur simultaneously in the latent space, eliminating the sequential dependency and reducing overall processing time compared to separate post-generation watermarking steps.
2Measurement precision
If post-generation watermarking is applied, then watermark extraction accuracy is improved, but image quality deteriorates
Solution Approach 1:
The patent embeds watermarks during the image generation process itself, before the image is completed. The watermark is integrated into the latent space representations during diffusion or generation steps, eliminating the need for separate post-generation watermarking operations and reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent applies different levels of watermark embedding at different stages of the generation process. By strategically placing watermark information in specific latent space layers and using adaptive embedding strengths, the system maintains high image quality while ensuring robust watermark extraction accuracy.
3Quantity of substance
If watermark capacity is increased, then more information can be embedded, but robustness against transformations deteriorates
Solution Approach 1:
The patent divides the watermark embedding process into multiple independent components distributed across different latent space layers. Each layer contains a portion of the watermark information, allowing the system to embed longer watermarks while maintaining robustness - if one layer is affected by transformation, other layers can compensate.
Solution Approach 2:
The patent utilizes the latent space dimensionality to embed watermark information. By transforming the watermark into the latent space representation domain rather than directly manipulating pixel space, the system can embed higher capacity watermarks that remain robust to common image transformations like cropping and scaling.
4Loss of time
If in-generation watermarking is applied, then processing time is reduced, but watermark extraction accuracy may deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms during the generation process to ensure watermark information is properly embedded. The system monitors the generation steps and adjusts the embedding strength and positioning to maintain optimal extraction accuracy while operating in the integrated in-generation mode.
Data Source
AI summary
Methods, devices, and processor-readable media for digital image watermarking, including performing one or more decoding operations that each include generating a respective intermediate watermark embedding feature tensor that includes one or more representations of a watermark message embedded into a feature tensor generated by a preceding decoding operation; upscaling the respective intermediate watermark embedding feature tensor to generate a further feature tensor; and generate a watermarked image based on the further feature tensor.