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A multi-part ISO/IEC series (Part 1 is a Technical Report, Part 2 an International Standard) outlining statistical, formal, and empirical 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, a Technical Report, gives background on existing robustness assessment methods, grouped into statistical, formal, and empirical approaches. 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.