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Recommended Practice for Vulnerability Test for Machine Learning Models for Computer Vision Applications

This recommended practice provides a framework for vulnerability tests for machine learning models in the computer vision domain. The document covers the following areas: - definitions of vulnerabilities for machine learning models and their training processes, - approaches for the selection and application of vulnerability test means, - approaches for determining test completeness and termination criteria, - metrics of vulnerabilities and test completeness.

Sponsor Committee
C/AISC - Artificial Intelligence Standards Committee
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PAR Approval

Working Group Details

IEEE Computer Society
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Sponsor Committee
C/AISC - Artificial Intelligence Standards Committee
Working Group
VTCV - Vulnerability Test for Machine Learning-based Computer Vision Applications
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IEEE Program Manager
Christy Bahn
Working Group Chair
Xiaoqi Cao
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