Classifier Ensemble Version Space Analysis for Adversarial Robustness
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Solution Overview
Problem
Existing machine learning technologies face challenges in understanding the behavior and robustness of ensembles of classifiers against perturbation-bounded evasion attacks, where adversarial examples can fool classifiers despite being trivial for humans.
Innovation Solution
A system that converts neural networks to equivalent linear models, maps parameters into version spaces, and measures diversity and robustness using metrics like Mahalanobis distance and statistical dispersion, analyzing the ensemble's behavior under adversarial perturbations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an ensemble of classifiers is used to defend against evasion attacks, then robustness against adversarial examples is improved, but understanding and measuring the behavior and robustness of the ensemble becomes more difficult
Solution Approach 1:
The patent introduces version space as an intermediary geometric representation that mediates between the complex ensemble of classifiers and the need for simple robustness measurement. By mapping classifiers to points in version space and adversarial examples to hyperplanes, the system creates a geometric intermediary that simplifies the analysis of ensemble behavior and provides intuitive measures of robustness through geometric distances.
2Adaptability or versatility
If multiple neural networks are converted to linear models to create an ensemble, then diversity of classifiers is increased, but complexity of the system increases
Solution Approach 1:
The patent extracts the essential classification behavior from complex neural networks by converting them to simpler linear models based on penultimate layer activations. This extraction process removes the complexity of deep network architectures while preserving the essential decision boundaries, allowing diversity to be measured through geometric properties of the extracted linear models in version space rather than through complex network comparisons.
3Measurement precision
If classifiers are represented as points in version space and adversarial examples as hyperplanes, then measurement precision of robustness is improved, but computational complexity increases
Solution Approach 1:
The patent replaces complex computational procedures for measuring robustness with geometric calculations in version space. Instead of evaluating each classifier individually against adversarial examples through complex neural network computations, the system uses geometric distances between points (classifiers) and hyperplanes (adversarial examples) in version space, substituting mechanical geometric reasoning for complex computational evaluation.
Data Source
AI summary
One embodiment provides a system which facilitates reasoning about classifiers. During operation, the system determines a plurality of neural networks. The system derives, from a respective neural network, a linear model, wherein the linear model is constructed based on an output of a penultimate layer of the respective neural network. The system trains the linear model based on activations of the penultimate layer. The system maps parameters of the trained linear model into a version space.