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
- Status
- Active PAR
- PAR Approval
- 2022-03-24
Working Group Details
- Society
- IEEE Computer Society
Learn More About IEEE Computer Society - Sponsor Committee
- C/AISC - Artificial Intelligence Standards Committee
- Working Group
-
VTCV - Vulnerability Test for Machine Learning-based Computer Vision Applications
Learn More About VTCV - Vulnerability Test for Machine Learning-based Computer Vision Applications - IEEE Program Manager
- Christy Bahn
Contact Christy Bahn - Working Group Chair
- Xiaoqi Cao
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