Fast and Flexible Robustness Certificates for Semantic Segmentation

Deep neural networks remain vulnerable to imperceptible ad-versarial perturbations, yet efficient robustness certification for seman-tic segmentation is largely unexplored. We propose the first real-timecompatible certifiably robust semantic segmentation framework basedon networks with built-in Lipschitz constraints. Our method achievescompetitive pixel accuracy on Cityscapes while providing determinis-tic robustness guarantees, without the computational overhead of ran-domized smoothing or formal verification. We introduce a generalizedcertification framework for segmentation that computes worst-case per-formance under ℓ2 attacks of radius ϵ across diverse evaluation metrics.Crucially, our approach is approximately 600× faster than randomizedsmoothing at inference on an NVIDIA A100 GPU, delivering comparablecertificates. We validate our worst-case bounds against state-of-the-artadversarial attacks, demonstrating that Lipschitz-constrained architec-tures offer a practical and scalable path toward trustworthy dense pre-diction in safety-critical applications.