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原文摘要

Lipschitz-based certification checks a neural network's robustness with a small numerical computation: milliseconds per input, and small enough to be formally verified. Its soundness is proved for execution in real arithmetic, whereas deployed networks execute in floating point, so rounding must be accounted for in the network and in the certifier itself. We show that the conservatism of Lipschitz-based certification does not absorb the rounding in the network's execution: we exhibit counterexamples to a formally verified real-arithmetic certifier that computes in exact arithmetic, and show that an adversary who controls the weights can make it issue false certificates for ordinary test images at standard perturbation bounds. We present the first Lipschitz-based certification method that is both efficient and sound with respect to floating-point execution. It keeps the real-arithmetic network's Lipschitz constant and bounds separately how far the floating-point outputs can depart from the real-arithmetic ones, through a compositional theory of the deviation that also rules out overflow; pre-deployment measurements of a high-precision execution tighten the bound. Its spectral norms come from a float64 Gram iteration proved never to under-estimate the true norm. We mechanise the theory in Rocq and implement it in a certifier, evaluated on dense image, tabular and many-class classifiers. It rejects every certificate a counterexample refutes and certifies entire test sets at milliseconds per instance on a CPU, losing relatively few real-arithmetic certificates, many of which the measurement reclaims. At float32, ERAN's GPUPoly, to our knowledge the only other public floating-point-sound verifier for networks of our size, loses most of its certificates on most of our models to its own rounding accounting; ours does not, and is more than an order of magnitude faster.