Watermarked Machine Learning Model Weights
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Solution Overview
Problem
Machine learning model weights are susceptible to theft and unauthorized alteration due to the high cost and difficulty of determining them during the training process, making protection against unauthorized use challenging.
Innovation Solution
The weights are scrambled and embedded with watermarks, requiring a watermark signature to unlock and unscramble them for authorized use, ensuring protection against unauthorized access and alteration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If machine learning model weights are made publicly available for authorized use, then model accessibility and functionality are improved, but security and protection against unauthorized use deteriorate
Solution Approach 1:
The patent applies preliminary action by embedding watermarks and scrambling weights before they are made available for use. The weights are pre-processed to include hidden watermark information and scrambled bits, so that unauthorized users cannot access or modify the weights without the proper unscrambling key. This preliminary security measure allows the weights to be shared publicly while maintaining protection against theft and unauthorized alteration.
2Reliability
If weights are scrambled and watermarked to protect against theft, then security is improved, but complexity of weight processing and verification increases
Solution Approach 1:
The patent applies segmentation by dividing the weight data into multiple partitions, where each partition contains a portion of the weight parameters. Watermarks are embedded in specific partitions while other partitions contain the actual functional weight data. This segmentation allows the verification system to efficiently check watermark integrity without processing the entire weight matrix, reducing the complexity of verification while maintaining security.
Solution Approach 2:
The patent uses an intermediary verification system that acts as a mediator between the scrambled weights and the user. The verification system checks watermarks and manages the unscrambling process, so that users only need to provide their identification and receive the unscrambled weights if authorized. This intermediary layer simplifies the user experience while maintaining complex security measures in the background.
3Reliability
If watermark bits are inserted into least significant bits of weights, then watermark embedding is improved, but precision and accuracy of weight values may deteriorate
Solution Approach 1:
The patent applies local quality by embedding watermarks in specific locations (least significant bits) of the weight data while preserving the precision of critical weight values in other locations. Different portions of the weight matrix have different requirements: some bits are dedicated to watermark embedding while other bits maintain the full precision needed for accurate model computations. This localized approach allows watermarking without sacrificing the numerical precision required for model functionality.
Data Source
AI summary
In some examples, a system includes storage storing a machine learning model, wherein the machine learning model comprises a plurality of layers comprising multiple weights. The system also includes a processing unit coupled to the storage and operable to group the weights in each layer into a plurality of partitions; determine a number of least significant bits to be used for watermarking in each of the plurality of partitions; insert one or more watermark bits into the determined least significant bits for each of the plurality of partitions; and scramble one or more of the weight bits to produce watermarked and scrambled weights. The system also includes an output device to provide the watermarked and scrambled weights to another device.