SNZ TR ISO/IEC 24029.1:2025
Artificial Intelligence (AI) – Assessment of the robustness of neural networks – Part 1: Overview
Technical report SNZ TR ISO/IEC 24029.1:2025, Artificial Intelligence (AI) – Assessment of the robustness of neural networks – Part 1: Overview is an identical adoption of ISO/IEC TR 24029-1:2021. It provides an overview of methods for assessing the robustness of neural networks in artificial intelligence (AI) systems and sets the foundation for more detailed methodologies, which are covered in NZS ISO/IEC 24029.2.
NZS ISO/IEC 24029.2:2025, Artificial intelligence (AI) – Assessment of the robustness of neural networks – Part 2: Methodology for the use of formal methods is an identical adoption of ISO/IEC 24029-2:2023. It aims to support developers, researchers, and regulators in ensuring that AI systems – especially those using neural networks – are safe, reliable, and resilient to unexpected or malicious inputs.
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SNZ TR ISO/IEC 24029.1 2025
Committee representation
Acknowledgement
Copyright
Preface
Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
4 Overview of the existing methods to assess the robustness of neural networks
4.1 General
4.1.1 Robustness concept
4.1.2 Typical workflow to assess robustness
4.2 Classification of methods
5 Statistical methods
5.1 General
5.2 Robustness metrics available using statistical methods
5.2.1 General
5.2.2 Examples of performance measures for interpolation
5.2.3 Examples of performance measures for classification
5.2.4 Other measures
5.3 Statistical methods to measure robustness of a neural network
5.3.1 General
5.3.2 Contrastive measures
6 Formal methods
6.1 General
6.2 Robustness goal achievable using formal methods
6.2.1 General
6.2.2 Interpolation stability
6.2.3 Maximum stable space for perturbation resistance
6.3 Conduct the testing using formal methods
6.3.1 Using uncertainty analysis to prove interpolation stability
6.3.2 Using solver to prove a maximum stable space property
6.3.3 Using optimization techniques to prove a maximum stable space property
6.3.4 Using abstract interpretation to prove a maximum stable space property
7 Empirical methods
7.1 General
7.2 Field trials
7.3 A posteriori testing
7.4 Benchmarking of neural networks
Annex A (informative) Data perturbation
Annex B (informative) Principle of abstract interpretation
Bibliography
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SNZ TR ISO/IEC 24029.1 2025
Committee representation
Acknowledgement
Copyright
Preface
Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
4 Overview of the existing methods to assess the robustness of neural networks
4.1 General
4.1.1 Robustness concept
4.1.2 Typical workflow to assess robustness
4.2 Classification of methods
5 Statistical methods
5.1 General
5.2 Robustness metrics available using statistical methods
5.2.1 General
5.2.2 Examples of performance measures for interpolation
5.2.3 Examples of performance measures for classification
5.2.4 Other measures
5.3 Statistical methods to measure robustness of a neural network
5.3.1 General
5.3.2 Contrastive measures
6 Formal methods
6.1 General
6.2 Robustness goal achievable using formal methods
6.2.1 General
6.2.2 Interpolation stability
6.2.3 Maximum stable space for perturbation resistance
6.3 Conduct the testing using formal methods
6.3.1 Using uncertainty analysis to prove interpolation stability
6.3.2 Using solver to prove a maximum stable space property
6.3.3 Using optimization techniques to prove a maximum stable space property
6.3.4 Using abstract interpretation to prove a maximum stable space property
7 Empirical methods
7.1 General
7.2 Field trials
7.3 A posteriori testing
7.4 Benchmarking of neural networks
Annex A (informative) Data perturbation
Annex B (informative) Principle of abstract interpretation
Bibliography
SNZ TR ISO/IEC 24029.1:2025
Online Library subscription information
Online library subscriptions provide a cost-effective solution for organisations of any size to access the latest standards, offering 24/7 access to a personalised catalogue. The service ensures subscribers have have the most up-to-date version of selected standards, incluing automatic updates for any amendments or versions. Pricing is dependent on the chosen standards and the number of concurrent users, with discounts applied from the standards PDF price. For customised subscriptions or further inquiries, contact the Online Library team.