Machine Learning Model Watermarking with Trigger Sample Embedding
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Solution Overview
Problem
Existing methods for watermarking machine learning models are challenging due to the need to balance embedding the watermark within the model's functionality while keeping it hidden from attackers, and ensuring it remains detectable and difficult to remove or modify when the model is copied.
Innovation Solution
A method for watermarking a machine learning model involves training the model with supervised learning using trigger samples created from labeled training input samples. These trigger samples are generated by combining chunks of text characters with input samples, and are relabeled to maintain domain relevance, helping to embed a watermark that is detectable in copies of the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a watermark is embedded into a ML model by training with watermark information, then the watermark is detectable in copies, but the functionality of the model is altered
Solution Approach 1:
The training data is segmented into two distinct parts: a large set of normal training data and a small set of trigger samples. The trigger samples are specifically designed to embed the watermark without significantly impacting the overall model functionality. This segmentation allows the watermark to be embedded while preserving the model's primary function through the dominance of normal training data.
Solution Approach 2:
The watermark is embedded locally through trigger samples that have specific characteristics (e.g., particular input patterns or feature combinations) rather than uniformly across all training data. These trigger samples are designed to activate specific neural pathways or weight configurations that encode the watermark, allowing detection without altering the model's general behavior on normal inputs.
2Object-affected harmful factors
If the watermark is made hidden from attackers, then security is improved, but detection difficulty increases
Solution Approach 1:
The trigger samples act as an intermediary mechanism between the watermark embedding and detection. These samples serve as a bridge that encodes the watermark in a way that is not directly visible in the model parameters but can be extracted through specific input patterns. The trigger samples mediate between the need for hidden watermarks and the need for detectable signals.
Solution Approach 2:
The watermark is embedded through subtle changes in the model's behavior or output characteristics rather than obvious modifications. Similar to color changes being imperceptible at first glance but detectable with the right tools, the watermark is embedded in ways that are imperceptible to attackers inspecting model parameters directly, yet detectable through trigger sample analysis.
3Device complexity
If trigger samples are created from a small portion of training data, then the watermark embedding is simplified, but the model accuracy may be compromised
Solution Approach 1:
Instead of using all training data for watermark embedding, the method uses only a partial subset (the trigger samples). This partial action approach simplifies the embedding process while the effectiveness is amplified by the strategic selection of trigger samples that, when combined with normal training data, maintain sufficient model accuracy. The partial data approach is compensated by the high impact of each trigger sample.
Data Source
AI summary
A method is provided for watermarking a machine learning model. A sequence of bits is generated. The sequence of bits may be text characters divided into chunks. A selected plurality of input samples from training data is divided into subsets of input samples. All of the input samples of each subset of the subsets of input samples are labeled with a same first label in a problem domain of the ML model. Each chunk is combined with a subset of the labeled subsets to produce a plurality of labeled trigger samples. Each trigger sample of each set of the plurality of sets is relabeled to have a second label different from the first label and in the problem domain to produce a relabeled set of trigger samples. The ML model is trained with the training data and the relabeled trigger samples to produce a watermarked ML model.