Machine Learning Model Integrity Verification via Serialization
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Solution Overview
Problem
Machine learning models can become corrupted due to hardware and software failures, leading to incorrect outputs that can cause deleterious effects in computer networks, and existing systems lack real-time integrity verification capabilities.
Innovation Solution
A system that determines whether the output of a machine learning model corresponds to a reference output by serializing the model into a file, calculating a file integrity value using a hash function, and comparing it to a reference integrity value, allowing for the verification of model integrity and ensuring correct operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine learning models are stored in main memory for runtime operations, then accessibility and speed are improved, but integrity verification capability deteriorates
Solution Approach 1:
The system segments the machine learning model into two parts: the model itself stored in main memory for fast runtime operations, and a separate serialized version stored in a file for integrity verification. This segmentation allows the model to be quickly accessible while its integrity can be independently verified without affecting runtime performance.
Solution Approach 2:
The patent introduces an intermediary integrity verification mechanism that uses a serialized copy of the model and hash function calculations. This intermediary system verifies model integrity without requiring direct access to the runtime model in memory, thus maintaining both speed and reliability.
2Reliability
If integrity verification mechanisms are implemented, then model corruption detection is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by serializing the machine learning model and calculating its hash value before runtime operations begin. This pre-computed serialized version and reference hash value are stored for later verification, eliminating the need for complex real-time verification mechanisms during model execution.
Solution Approach 2:
The patent replaces complex mechanical or procedural verification methods with a computational approach using hash functions. Instead of implementing intricate verification algorithms, the system uses straightforward hash calculation and comparison operations to verify model integrity, significantly reducing system complexity.
3Loss of time
If real-time verification is performed, then corruption detection speed is improved, but processing overhead increases
Solution Approach 1:
The system performs the computationally intensive serialization and hash calculation operations before runtime, storing the results for quick comparison during verification. This preliminary action shifts the processing overhead to model loading time rather than runtime, enabling fast real-time verification with minimal energy consumption.
Data Source
AI summary
Provided is a system for verifying integrity of a machine learning model, the system includes at least one processor programmed or configured to determine whether an output of a machine learning model based on an input corresponds to a reference output of the machine learning model based on the input, serialize the machine learning model into a file, calculate a file integrity value of the file using a file integrity detection function, determine whether the file integrity value corresponds to a reference file integrity value of the file, and perform an operation with the machine learning model based on determining that the file integrity value corresponds to the reference file integrity value of the file. A method and computer program product are also disclosed.