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A multi-part international standard outlining formal methods and statistical approaches for evaluating the robustness of artificial neural networks.

Robustness testing ensures deep neural networks perform predictably under noise, distribution shifts, and malicious adversarial perturbations in mission-critical applications.

An autonomous vehicle supplier subjects computer vision neural networks to formal verification and statistical perturbation testing in accordance with ISO/IEC 24029 before field deployment.

Formal mathematical verification is computationally intractable for very large neural architectures; statistical robustness testing cannot provide absolute safety proofs across unconstrained operational domains.

Part 1 provides an overview of robustness concepts and statistical testing methods against input perturbations and noise. Part 2 defines formal verification methods—including constraint solving, abstract interpretation, and reachability analysis—to mathematically prove neural network output stability within defined perturbation boundaries.