Quantized LLM Watermarking for Robust IP Protection
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Solution Overview
Problem
Existing methods for watermarking large language models (LLMs) are computationally intensive and difficult to scale due to the large number of parameters, and they lack robustness against removal and forging attacks, especially on resource-constrained devices.
Innovation Solution
A computationally efficient method for inserting digital signatures into quantized LLM parameters, using post-training quantization and strategic weight selection based on sensitivity and saliency scores to ensure robustness against attacks, while maintaining model quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If watermarking is applied to protect intellectual property of large language models, then security and reliability are improved, but computational complexity and device requirements worsen
Solution Approach 1:
The patent segments the watermarking process into distinct phases: training-time watermark insertion during model development and inference-time watermark extraction during deployment. This segmentation allows the computationally intensive watermark embedding to be performed once during training, while the extraction phase requires minimal computation during inference, thus improving reliability without proportionally increasing device complexity.
Solution Approach 2:
The watermark is embedded into the model parameters during the training phase before deployment. This preliminary action ensures that the watermark is already integrated into the model structure, eliminating the need for complex real-time watermark embedding during inference on resource-constrained devices, thereby maintaining security while reducing computational complexity at deployment.
2Reliability
If full-precision floating point weights are used for watermarking, then watermark robustness is improved, but memory usage and computational overhead worsen
Solution Approach 1:
The patent changes the parameter representation from full-precision floating point to quantized formats (e.g., INT8, INT4) while maintaining watermark robustness. The watermark embedding process is designed to work with quantized weights by applying watermark signals in a manner that preserves robustness even at lower precision, thus reducing computational overhead and memory usage without sacrificing watermark reliability.
Solution Approach 2:
The patent applies different watermarking strategies to different parts of the model based on their importance. Critical layers that contribute most to model performance receive more robust watermark embedding, while less critical layers use lighter watermarking approaches. This local differentiation maintains overall watermark robustness while reducing total computational overhead.
3Productivity
If quantization is applied to reduce model size, then deployment efficiency is improved, but watermark robustness against removal attacks worsens
Solution Approach 1:
The watermark is embedded into the model parameters during the training phase before quantization is applied. This preliminary embedding ensures that the watermark is integrated at the full-precision level, and subsequent quantization operations preserve the watermark structure. The watermark robustness is maintained because the embedding occurs before the quantization that might otherwise degrade it, allowing efficient quantized deployment without sacrificing reliability.
Solution Approach 2:
The patent incorporates robustness-cushioning techniques by embedding the watermark with sufficient strength during training to withstand subsequent quantization operations. The watermark signal is designed with margins that accommodate the information loss from quantization, ensuring that even after aggressive quantization for deployment efficiency, the watermark remains detectable and robust against removal attacks.
4Reliability
If watermarking is applied to quantized models, then IP protection is maintained, but model performance and accuracy worsen
Solution Approach 1:
The patent applies watermarking selectively to specific layers and parameters based on their contribution to model performance. Critical parameters that heavily influence accuracy are either excluded from watermark embedding or receive weaker watermark signals, while less critical parameters receive stronger watermarking. This local differentiation maintains model accuracy while providing sufficient IP protection through watermarking in non-critical regions.
Solution Approach 2:
The patent applies partial watermarking rather than embedding watermarks in all model parameters. By selecting only a subset of parameters for watermark embedding (e.g., specific layers or weight matrices), the patent achieves adequate IP protection while minimizing the impact on model accuracy. The partial action approach ensures that the most performance-critical parameters remain untouched, preserving model quality.
Data Source
AI summary
In some embodiments, there is provided a computer-implemented method for watermarking selected weights of a machine learning model. In some embodiments, a method includes receiving a quantized machine learning model comprising a plurality of layers associated with a plurality of weights; determining, for each of the plurality of weights, a corresponding score indicative of an effect of the corresponding weight on an output of the quantized machine learning model; selecting, based on the scores, a set of the plurality of weights having a corresponding score below a threshold; selecting, from the set of the plurality of weights, a subset of the plurality of weights for insertion of a signature; and inserting a signature on each of the weights of the subset of the plurality of weights. Related systems, methods, and articles of manufacture are also disclosed.