Lipschitz-Based Robustness Certification Under Floating-Point Execution

Lipschitz-based robustness certification bounds a network's sensitivity through concrete numerical computation rather than symbolic reasoning, and so scales efficiently. It is increasingly used even where verifiable guarantees matter. Yet, as with most prior work on robustness certification and verification, soundness is typically proved against a semantic model assuming exact real arithmetic. Deployed networks instead execute in floating-point, creating a gap between certified properties and executed behaviour. As motivating evidence, we give counterexamples showing that real arithmetic robustness guarantees can fail under floating-point execution, even for previously verified certifiers. We then develop a formal, compositional theory relating real arithmetic Lipschitz-based sensitivity bounds to floating-point execution under standard rounding-error models for feed-forward ReLU networks. We derive sound conditions for floating-point robustness, including bounds on certificate degradation and sufficient conditions for the absence of overflow. We also give an efficient floating-point Gram iteration algorithm for Lipschitz bounds and prove that it never under-estimates the true norm. Separately, when a model is certified pre-deployment, we show how measuring its actual deviation against a high-precision execution can substantially reduce certificate degradation. We formalise the theory and its soundness, and implement an executable certifier, evaluated across dense networks spanning image, tabular, and many-class classification. To our knowledge, ours is the first method for soundly accounting for floating-point effects in Lipschitz-based robustness certification, and, done efficiently, the first floating-point-sound robustness checking procedure of any kind to certify models' entire test set -- seven those with 500,000 examples -- while retaining enough precision to be practical.