Computational Graph Normalization for ML Backdoor Detection
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Solution Overview
Problem
Machine learning models contain computational graphs that can be exploited for malicious purposes, with techniques to insert backdoors and malicious behavior that cannot be removed without compromising the models' functionality.
Innovation Solution
Extract the computational graph from the machine learning model, convert it into a normalized format, decompose it into components, and scan for backdoors using signature comparison and machine learning techniques to identify and remediate vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If computational graphs are used in machine learning models for easier production deployment, then ease of operation is improved, but security vulnerabilities and backdoor insertion risks worsen
Solution Approach 1:
The system performs preliminary security scanning and validation of computational graphs before deployment. The vulnerability detection system analyzes the computational graph structure in advance, identifying potential backdoors and malicious operations before the model is deployed to production, thereby preventing harmful factors from affecting the system.
Solution Approach 2:
The patent introduces an intermediary vulnerability detection system that acts as a mediator between the computational graph and the production environment. This intermediary system scans and validates the computational graph, filtering out malicious elements while allowing legitimate operations to pass through, thus resolving the contradiction between ease of deployment and security.
2Adaptability or versatility
If computational graphs include nodes for model functionality, then model operation capability is improved, but susceptibility to malicious exploitation worsens
Solution Approach 1:
The vulnerability detection system applies local quality analysis by examining specific nodes and operations within the computational graph individually. Each node is analyzed for malicious characteristics while preserving the overall functionality of the model. This allows the system to maintain adaptability by keeping legitimate nodes while removing or flagging malicious ones.
Solution Approach 2:
The computational graph is segmented into individual nodes and operations for separate analysis. The system divides the complex computational graph into manageable components, scanning each segment for backdoors and vulnerabilities. This segmentation enables the model to maintain its operational capability while systematically identifying and removing malicious elements from specific segments.
3Measurement precision
If computational graphs are converted to normalized format for analysis, then detection accuracy is improved, but processing time and complexity worsen
Solution Approach 1:
The system changes the parameter representation of the computational graph by converting it to a normalized format with standardized node types and operations. This parameter transformation enables more accurate detection by allowing consistent pattern matching across different models. The processing time penalty is accepted as necessary to achieve the improved detection accuracy and reliability.
Data Source
AI summary
Vulnerabilities in a machine learning model can be identified by receiving at least one file encapsulating the machine learning model. A computational graph corresponding to the machine learning model is then extracted from the at least on file. The computational graph is converted from a first format into a normalized computational graph having a second, different format. The normalized computational graph is decomposed into components. These components can include nodes, blocks, and edges between blocks. The normalized computational graph is scanned by iterating through the components to identify any backdoors. Data characterizing whether any backdoors were identified can be provided to a consuming application or process.