ML Model Watermarking via Training Data Pixel Pattern Variation
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Solution Overview
Problem
Machine learning models can be illegitimately extracted and cloned with black box access, leading to unauthorized use and monetization, and existing watermarking methods are difficult and costly to implement effectively.
Innovation Solution
A method for watermarking a machine learning model by modifying a subset of labeled training data with a varying pixel pattern, which is then integrated into the model during training, making it difficult for attackers to extract and allowing for detection of the watermark during inference operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a watermark is embedded into a ML model by training with watermark information, then ownership proof is achieved, but the model's functionality may be altered
Solution Approach 1:
The training data is segmented into two distinct subsets: a first subset containing watermark information and a second subset containing normal training data. This segmentation allows the model to learn both watermark detection and normal functionality separately, resolving the contradiction between ownership proof and model functionality.
Solution Approach 2:
Different parts of the training data serve different purposes: the first subset (with pixel patterns) is dedicated to watermark embedding while the second subset maintains normal training functions. This local differentiation ensures that watermarking does not compromise overall model functionality.
2Difficulty of detecting and measuring
If a watermark is made detectable, then ownership verification is enabled, but the watermark becomes vulnerable to removal or modification
Solution Approach 1:
The watermark is embedded during the preliminary training phase through careful construction of training data with specific pixel patterns. This preliminary embedding creates a robust watermark that is detectable yet resistant to removal, as it is integrated into the model's fundamental learning process rather than added as a separate layer.
3Reliability
If effective watermarking is implemented, then model protection is achieved, but implementation becomes difficult and costly
Solution Approach 1:
The watermarking method uses copies of pixel patterns that are systematically varied and embedded in training data. This copying approach allows for automated watermark generation and integration, reducing manual intervention and lowering implementation costs while maintaining protection effectiveness.
Data Source
AI summary
A method is provided for watermarking a machine learning model. In the method, a first subset of a labeled set of ML training samples is selected. The first subset is of a predetermined class of images. A first pixel pattern is selected and inserted into each sample of the first subset. One or more of a location, position, orientation, and transformation of the first pixel pattern is varied for each of the samples. Each sample of the first subset is relabeled to have a different label than the original label. The ML model is trained with the labeled set of ML training samples and the first subset of relabeled ML training samples. To detect the watermark, a second subset of training samples is selected, and the first pixel pattern is inserted into each sample. The second subset is used during inference operation to detect the presence of the watermark.