Digital Watermarking for Machine Learning Model Ownership Protection
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Solution Overview
Problem
Machine learning models lack effective methods to detect and prevent misuse, as they are often deployed without indicia of ownership, making it difficult to track and prevent unauthorized use or training with proprietary data.
Innovation Solution
A digital watermark is embedded in machine learning models, specifically in hidden layers or output layers, using training data that alters low probabilistic regions of activation maps, allowing the owner to identify duplicates or unauthorized use by comparing bit error rates or output patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a digital watermark is embedded in the machine learning model by training with additional watermarking data, then the model's ownership protection capability is improved, but the training time and computational resources are increased
Solution Approach 1:
The digital watermark is embedded in the machine learning model during the initial training phase by incorporating watermarking training data into the training dataset. This preliminary action ensures that the watermark is already present in the model weights when deployed, eliminating the need for separate watermark embedding operations later and reducing overall time loss.
Solution Approach 2:
The watermark embedding process is merged with the model training process by combining the original training data with additional watermarking training data. This consolidation allows the model to learn both the task functionality and the watermark signature simultaneously, reducing redundant computational steps and optimizing resource utilization.
2Reliability
If a digital watermark is embedded in the machine learning model, then the ability to detect and prevent misuse is improved, but the model complexity increases
Solution Approach 1:
The digital watermark serves as a copied signature embedded within the model weights that can be extracted and verified independently. This copying approach allows ownership verification without modifying the core functional structure of the model, maintaining simplicity while enhancing security capabilities.
Solution Approach 2:
The digital watermark acts as an intermediary element that bridges ownership protection and model functionality. It is embedded in the model weights but can be extracted and verified separately, allowing misuse detection without interfering with the model's primary computational operations or increasing operational complexity.
3Reliability
If training data is selected to alter low probabilistic regions of activation maps for watermark embedding, then the watermark robustness is improved, but the manufacturing precision of the training process is increased
Solution Approach 1:
The training process focuses on altering specific low probabilistic regions of the activation maps rather than uniformly modifying all activations. This localized approach concentrates the watermark embedding effort in targeted areas where neurons have lower activation probabilities, enhancing watermark robustness while limiting the overall impact on model training precision.
Solution Approach 2:
The method utilizes changes in activation probability parameters during training to embed the watermark. By selecting training samples that produce low probabilistic activations and adjusting these specific parameter regions, the system achieves robust watermark embedding without requiring high-precision control over the entire training process, thus balancing robustness with training feasibility.
Data Source
AI summary
A method may include embedding, in a hidden layer and/or an output layer of a first machine learning model, a first digital watermark. The first digital watermark may correspond to input samples altering the low probabilistic regions of an activation map associated with the hidden layer of the first machine learning model. Alternatively, the first digital watermark may correspond to input samples rarely encountered by the first machine learning model. The first digital watermark may be embedded in the first machine learning model by at least training, based on training data including the input samples, the first machine learning model. A second machine learning model may be determined to be a duplicate of the first machine learning model based on a comparison of the first digital watermark embedded in the first machine learning model and a second digital watermark extracted from the second machine learning model.