ML Model Output Signatures for Embedded Identification
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Solution Overview
Problem
Existing machine learning models lack the ability to identify and log access to their own models without additional metadata, making it difficult to determine the model's origin and usage history.
Innovation Solution
Configuring machine learning models to embed identifying data within their output, allowing downstream systems to identify the model even without additional metadata, by setting aside certain bit locations for a signature that includes information like model author, version, and source.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If machine learning models use additional metadata to identify and log access, then model identification capability is improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent merges the model identification signature directly into the model weights themselves, eliminating the need for separate metadata structures. The signature is embedded by modifying specific bits within the weight values, so that the identification information becomes an integral part of the model parameters rather than an external addition.
Solution Approach 2:
The model weights serve dual functions: they perform the primary machine learning inference task while simultaneously encoding the model identification signature. This multi-functionality allows the same data structure to provide both computational functionality and identification capability without requiring additional specialized structures.
2Loss of information
If machine learning models embed identifying data within output, then model identification capability is improved, but manufacturing precision and training complexity increase
Solution Approach 1:
The patent segments the model weights into two functional parts: significant bits that perform the primary inference function and least significant bits that encode the identification signature. This segmentation allows the training process to focus on optimizing the significant bits while the least significant bits are used for signature embedding, thereby maintaining training precision while achieving identification capability.
Solution Approach 2:
The patent changes the parameter representation by utilizing the least significant bits of weight values for signature encoding. This parameter transformation allows the same weight structure to serve dual purposes without compromising the precision of the primary inference function, as the significant bits remain dedicated to accurate computation.
3Loss of information
If machine learning models output encoded signature, then model identification and logging capability is improved, but productivity and processing speed may decrease
Solution Approach 1:
The patent applies partial action by only modifying and utilizing the least significant bits of the weight values for signature embedding, while leaving the significant bits unchanged for inference operations. This partial modification approach minimizes the impact on processing speed while still achieving the identification capability, as the critical computational paths remain unaltered.
Data Source
AI summary
A system and techniques for configuring a trained model to dedicate a portion of its output data to include signature data that may be used to identify information about the model. The model may be configured so that some portion of its least-significant output bits may represent the signature data. The signature data may be a unique code that corresponds to the particular model. The signature data may also include encoded data that may represent information such as a model version, model author, or the like. A recipient of the model output data may thus use the signature data to determine information about the particular model, even if the model itself is inaccessible.