AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems

TL;DR

AI-Compass is a multi-module testing tool that thoroughly evaluates AI systems' robustness, interpretability, and neuron behavior across different modalities, addressing current limitations in comprehensive testing.

Contribution

The paper introduces AI-Compass, a novel comprehensive testing tool that assesses multiple aspects of AI systems simultaneously, improving over existing ad-hoc testing methods.

Findings

AI-Compass effectively evaluates adversarial robustness, interpretability, and neuron analysis.

The tool demonstrates state-of-the-art performance across image, object detection, and text classification tasks.

Extensive experiments validate its comprehensive assessment capabilities.

Abstract

AI systems, in particular with deep learning techniques, have demonstrated superior performance for various real-world applications. Given the need for tailored optimization in specific scenarios, as well as the concerns related to the exploits of subsurface vulnerabilities, a more comprehensive and in-depth testing AI system becomes a pivotal topic. We have seen the emergence of testing tools in real-world applications that aim to expand testing capabilities. However, they often concentrate on ad-hoc tasks, rendering them unsuitable for simultaneously testing multiple aspects or components. Furthermore, trustworthiness issues arising from adversarial attacks and the challenge of interpreting deep learning models pose new challenges for developing more comprehensive and in-depth AI system testing tools. In this study, we design and implement a testing tool, \tool, to comprehensively and effectively evaluate AI systems. The tool extensively assesses multiple measurements towards adversarial robustness, model interpretability, and performs neuron analysis. The feasibility of the proposed testing tool is thoroughly validated across various modalities, including image classification, object detection, and text classification. Extensive experiments demonstrate that \tool is the state-of-the-art tool for a comprehensive assessment of the robustness and trustworthiness of AI systems. Our research sheds light on a general solution for AI systems testing landscape.