Test specifications with a set of indicators for common corruption and adversarial attacks, which can be used to evaluate the robustness of artificial intelligence-based image recognition services are provided in this standard. Robustness attack threats and establishes an assessment framework to evaluate the robustness of artificial intelligence-based image recognition service under various settings are also specified in this standard.
- Sponsor Committee
- C/AISC - Artificial Intelligence Standards Committee
- Status
- Active Standard
- PAR Approval
- 2021-11-09
- Board Approval
- 2023-02-15
- History
-
- Published:
- 2023-06-02
Working Group Details
- Society
- IEEE Computer Society
Learn More About IEEE Computer Society - Sponsor Committee
- C/AISC - Artificial Intelligence Standards Committee
- Working Group
-
RAIBS - Robustness of Artificial Intelligence Based Service
Learn More About RAIBS - Robustness of Artificial Intelligence Based Service - IEEE Program Manager
- Christy Bahn
Contact Christy Bahn - Working Group Chair
- Qing An
Other Activities From This Working Group
Current projects that have been authorized by the IEEE SA Standards Board to develop a standard.
P3168
Standard for Robustness Evaluation Test Methods for a Natural Language Processing Service that uses Machine Learning
This standard specifies test methods for evaluating the robustness of a Natural Language Processing (NLP) service that uses machine learning.nnModels of NLP generally feature an input space being discrete and an output space being almost infinite in some tasks. The robustness of the NLP service is affected by various perturbations including adversarial attacks. A methodology to categorize the perturbations, and test cases for evaluating the robustness of an NLP service against different perturbation categories is specified. Metrics for robustness evaluation of an NLP service are defined. NLP use cases and corresponding applicable test methods are also described.
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