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 model 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 iterative scanning and machine learning techniques to identify and remediate vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computational nodes are retained in the machine learning model to maintain runtime functionality, then the model can execute correctly, but security vulnerabilities and backdoors cannot be removed
Solution Approach 1:
The computational graph is segmented into individual nodes and edges, allowing the scanning system to analyze each component separately. This segmentation enables the identification of specific malicious nodes without requiring removal of the entire graph structure, thus maintaining model functionality while eliminating security vulnerabilities.
Solution Approach 2:
An intermediary scanning system is introduced between the computational graph and the model execution. This intermediary analyzes the graph structure, identifies malicious nodes through pattern matching and machine learning, and enables remediation while preserving the legitimate computational nodes required for model runtime functionality.
2Reliability
If the computational graph is scanned and analyzed to detect backdoors, then security vulnerabilities can be identified, but the scanning process increases computational time and resources
Solution Approach 1:
The scanning process focuses on critical components of the computational graph such as unusual nodes, edges with suspicious patterns, and specific subgraphs that are more likely to contain backdoors. By concentrating analysis on these high-risk areas rather than uniformly scanning every node, the system achieves effective security detection with reduced computational time.
Solution Approach 2:
Traditional exhaustive scanning methods are replaced with machine learning-based detection systems that can quickly identify malicious patterns. The use of trained models and pattern recognition algorithms substitutes for brute-force analysis, significantly reducing scanning time while maintaining or improving detection accuracy.
3Measurement precision
If the computational graph is converted to a normalized format for scanning, then vulnerability detection accuracy improves, but the conversion process adds complexity to the system
Solution Approach 1:
The normalized computational graph format serves multiple functions: it standardizes the representation for consistent scanning, enables pattern matching across different model formats, and facilitates machine learning analysis. This universal format acts as an intermediary representation that improves detection accuracy while the conversion process is managed as a standardized preprocessing step.
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.