Model Attack Evaluation Using Intention-Pattern Sample Generation
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Solution Overview
Problem
Current methods for evaluating machine learning model performance rely heavily on collected attack examples, which may lack diversity and number, leading to inaccurate assessments of the model's anti-attack capabilities.
Innovation Solution
Decouple the model attack into attack intention and pattern samples, generating a test sample set based on these to evaluate the model's anti-attack performance, ensuring improved sample diversity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If attack examples are collected for model evaluation, then the evaluation can be performed, but the number and diversity of samples remain insufficient leading to inaccurate assessments
Solution Approach 1:
The patent segments the attack sample construction process into two independent parts: attack intention samples and attack pattern samples. By dividing the sample generation into these two segments that can be independently collected and then combined through Cartesian product, the system achieves a much larger number of diverse test samples without requiring proportionally more manual annotation resources.
Solution Approach 2:
The patent creates a universal framework where attack intention samples and attack pattern samples can be independently collected from different sources and combined in multiple ways. This multi-functional approach allows the same set of intention samples to be paired with different pattern samples, generating diverse attack scenarios without duplicating the collection effort for each specific attack type.
2Adaptability or versatility
If more attack examples are collected to improve diversity, then sample diversity increases, but the collection cost and time increase significantly
Solution Approach 1:
By segmenting the sample construction into independent intention and pattern components, the patent allows parallel collection of both types of samples. This segmentation enables the system to achieve high diversity through combination rather than through extensive individual sample collection, significantly reducing the time required.
Solution Approach 2:
The patent performs preliminary action by collecting and storing attack intention samples and attack pattern samples separately before the actual evaluation. These pre-collected samples can then be rapidly combined through Cartesian product to generate numerous diverse test samples without requiring additional collection time during the evaluation phase.
3Reliability
If traditional attack samples are used for evaluation, then the process is simple, but the evaluation results lack reliability due to insufficient sample diversity
Solution Approach 1:
The patent segments the evaluation system into distinct modules: an obtaining module for collecting intention and pattern samples, a generation module for creating test samples through Cartesian product, and an evaluation module for assessing model performance. This segmentation improves reliability through systematic comprehensive testing while managing complexity through modular design.
Solution Approach 2:
The patent uses copying by generating multiple variations of attack samples through the Cartesian product of intention and pattern samples. Instead of manually creating each unique attack scenario, the system copies and combines existing sample components to generate a comprehensive set of test cases, ensuring reliable evaluation without proportionally increasing system complexity.
Data Source
AI summary
According to the embodiments of the disclosure, a method, an apparatus, a device, and a storage medium for model performance evaluation are provided. The method includes obtaining an intention sample set and a pattern sample set for model attack, where the intention sample set includes one or more attack intention samples related to the model attack, and the pattern sample set includes one or more attack pattern samples for a model. A first test sample set is generated based on an attack intention sample included in the intention sample set and an attack pattern sample included in the pattern sample set, where a test sample in the first test sample set has an attack intention and an attack pattern. An evaluation result of an anti-attack performance of a target model is determined based on output data of the target model executing the first test sample set.