ISO/IEC TR 24029-2:2023 AI Neural Network Robustness Assessment Management System

Formal Method for Evaluating the Robustness of Neural Networks

1. Standard positioning and objectives
ISO/IEC TR 24029-2:2023 belongs to the series of standards for robustness evaluation of artificial intelligence neural networks, aiming to quantitatively analyze the performance stability of neural networks under different input conditions through mathematical and logical tools (i.e. formal methods). Its core objectives include:
• Accurate validation: Ensure that the neural network meets specific security attributes (such as output error bounds, noise resistance, etc.) within a preset input range.
• Risk identification: Revealing the sensitivity of the network to input disturbances, adversarial attacks, and environmental changes, providing a basis for optimizing model architecture.
• Standardization process: Unify evaluation terminology, processes, and indicators to enhance the comparability and repeatability of industry evaluation results.

2. Application framework of formal methods
This standard proposes an evaluation process based on formal methods, which includes the following key steps:
(1) Model abstraction: Transforming neural networks into mathematical representations (such as tensor operation graphs, logical constraint models) to ensure their behavior can be accurately described by mathematical tools.
(2) Attribute definition: Clearly define the robustness attributes that need to be verified (such as output deviation not exceeding a threshold under input disturbance), and express them through logical formulas or inequalities.
(3) Formal verification: Using tools such as automatic theorem proving and symbolic execution, verify whether the network satisfies preset properties under all possible inputs.
(4) Sensitivity analysis: Quantify the impact of input parameter changes on output, identify high-risk input areas and vulnerable nodes.
(5) Result report: Generate a standardized assessment report that includes validation conclusions, risk levels, and improvement recommendations.

3. Key technical indicators and tool support
The standard clearly lists the indicators that need to be considered during the evaluation process, including:
Error margin: The maximum allowable output deviation of a network under noise interference.
Coverage integrity: Formal verification of the degree of coverage of the input space.
Computational complexity: the time and resource consumption efficiency of the verification process.
At the same time, the standard recommends the use of professional tools such as linear programming solvers and symbolic neural network validation tools (such as Marabou and NeVer), and emphasizes that the evaluation team needs to have mathematical modeling, programming, and formal logical analysis abilities.

1. Compliance support
This standard echoes regulations such as the EU's Artificial Intelligence Act and the US NIST AI Risk Management Framework, helping companies meet compliance requirements for "high-risk AI systems" and reduce legal risks.

2. Technical optimization direction
Guide developers to build AI models with endogenous security
Provide reproducible benchmark testing solutions for third-party evaluation agencies
Promote the transformation of adversarial machine learning research from academia to industry implementation

3. Typical application scenarios
Autonomous driving: Adversarial road sign recognition testing to ensure the visual model's anti-interference ability
Medical imaging: X-ray film for sample detection to prevent clinical risks caused by misdiagnosis
Fintech: Defend against fraudulent trading patterns and enhance the anti fraud performance of risk control models

4. Scope of application
This standard applies to various types of neural network architectures and input data types, including but not limited to piecewise linear neural networks, binary neural networks, recurrent neural networks, and transformer neural networks. It covers multiple aspects of neural network robustness evaluation, such as the definition and evaluation criteria of properties such as stability, sensitivity, correlation, and reachability.         

1. Current challenges
The rapid evolution of dynamic attack technology exceeds the standard update frequency
The cross modal attack detection of multimodal AI systems has not been fully covered yet
The balance problem between defense measures and model efficiency

2. The direction of standard evolution
ISO/IEC has initiated the upgrade of TR 24029-2 to an international standard (IS), with the expected addition of:
Adversarial evaluation methods for generative AI (such as GPT, diffusion models)
Guidelines for Distributed Attack Defense in Federated Learning Scenarios
Robustness Testing Specification for Quantum Machine Learning Models

ISO/IEC TR 24029-2 establishes a quantifiable protection system for AI systems against adversarial attacks through a systematic methodology. For enterprises, following this standard is not only a choice for technological optimization, but also a necessary requirement to cope with the increasingly strict global AI regulation. It is recommended that organizations integrate robustness assessment into AI lifecycle management in accordance with ISO/IEC 23894 risk management processes, in order to achieve synergistic development of technological innovation and secure trustworthiness.

ISO/IEC TR 24029-2:2023 is a technical report on the use of formal methods to evaluate the robustness of neural networks, and is not a certification standard, therefore there is no concept of "processing flow". However, if you need to refer to a similar standard processing procedure, you can refer to the following general steps:
General processing procedure

1. Application preparation
• Self evaluation: Conduct self-assessment against standard requirements to identify deficiencies and shortcomings in the system or project.
• Choose a certification body: Choose a suitable certification body and understand its certification process and requirements.

2. Submit application
• Fill out the application form: Submit the certification application form, including the basic information of the organization, the scope of the certification application, etc.
• Provide relevant materials, such as product information, project documents, system operation records, etc.

3. Audit and Certification
• Document review: The certification body reviews the submitted documents to confirm whether they meet the standard requirements.
• On site audit: The certification body conducts on-site audits of the organization, including the actual operation of the system, the compliance of documents, etc.
• Non conformance rectification: In response to the non conformance identified during the audit, the organization shall rectify it within the prescribed time and submit a rectification report.
• Audit decision: The certification body makes the certification decision based on the audit results and rectification situation.

4. Certificate issuance
• Issuing certificates: Certification bodies issue relevant certificates to organizations, which are usually valid for three years.

It should be noted that ISO/IEC TR 24029-2:2023 itself does not involve certification, but provides evaluation methods and guidance. If you need to obtain the specific content of the standard, it is recommended to purchase the standard text through formal channels.