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.
- Standard Committee
- C/AISC - Artificial Intelligence Standards Committee
- Status
- Active PAR
- PAR Approval
- 2022-03-24
Working Group Details
- Society
- IEEE Computer Society
- Standard 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
Other Activities From This Working Group
Current projects that have been authorized by the IEEE SA Standards Board to develop a standard.
No Active Projects
Standards approved by the IEEE SA Standards Board that are within the 10-year lifecycle.
No Active Standards
These standards have been replaced with a revised version of the standard, or by a compilation of the original active standard and all its existing amendments, corrigenda, and errata.
No Superseded Standards
These standards have been removed from active status through a ballot where the standard is made inactive as a consensus decision of a balloting group.
No Inactive-Withdrawn Standards
These standards are removed from active status through an administrative process for standards that have not undergone a revision process within 10 years.
No Inactive-Reserved Standards