ML Model Watermarking via Least Significant Bit Embedding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for embedding information into machine learning models are inefficient and can degrade model performance, particularly when attempting to digitally mark or identify important nodes, as they often involve brute force parameter modifications that affect model accuracy.
Innovation Solution
The method involves using steganographic techniques to embed data into the parameters of machine learning models by converting messages into binary representations and embedding them into the least significant bits of model parameters, utilizing cryptographic algorithms and identifying 'maximal non-interfering bits' to minimize performance impact, thus allowing for digital marking without degrading model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If brute force method is used to mark a machine learning model by changing parameters, then the model can be digitally marked, but model performance degrades drastically
Solution Approach 1:
The patent applies local quality by selectively modifying only the least significant bits of specific model parameters that have minimal impact on model performance. Instead of brute force modification of any parameter, the system identifies and modifies only those bit positions that locally affect the model minimally, thereby achieving digital marking while preserving model reliability.
Solution Approach 2:
The patent employs parameter changes by modifying the bit representation of model parameters in a controlled manner. Specifically, it changes only the least significant bits of selected parameters, which allows digital watermarking while maintaining the overall parameter values and model performance within acceptable thresholds.
2Loss of information
If parameters of important nodes are modified to embed data, then data can be embedded into the model, but model accuracy degrades
Solution Approach 1:
The system applies local quality by differentiating between important and non-important nodes, and within each node, between significant and non-significant bits. It selectively embeds data only in the least significant bits of parameters from non-critical nodes or less critical positions, thereby preserving model accuracy while achieving data embedding.
Solution Approach 2:
The patent uses partial action by embedding data in only a subset of available parameters - specifically targeting least significant bits rather than all bits, and selecting only certain nodes rather than all nodes. This partial modification approach ensures that model accuracy is maintained while still achieving sufficient data embedding capacity.
3Quantity of substance
If more bits are modified to increase embedding capacity, then more data can be embedded, but model performance deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the model parameters into different segments based on their importance and bit significance. It segments parameters into critical and non-critical groups, and further segments bits into significant and least significant groups. Data is embedded only in the least significant segments, maximizing embedding capacity while preserving model performance in the significant segments.
Solution Approach 2:
The system uses parameter changes by modifying only specific bit positions of specific parameters rather than changing entire parameters. By changing only the least significant bits, it increases embedding capacity while keeping the overall parameter values and model performance within acceptable ranges.
Data Source
AI summary
Exemplary systems and methods are directed to embedding data into a machine learning model. A processing device executes program code for running a machine learning model, which has a plurality of parameter values. The processing device receives a message to be embedded into the machine learning model. The message is encrypted according to a set of keys of a cryptographic algorithm. The encrypted message is converted to a corresponding binary representation. The binary representation of the encrypted message is embedded into at least one of the one or more parameters of the machine learning model. The embedding operation modifies the at least one parameter value of the machine learning model.