Probabilistic Robustness Analysis in High Dimensional Space: Application to Semantic Segmentation Network

2025-09-15

AI Summary

Semantic segmentation networks (SSNs) deployed in safety-critical applications—such as medical imaging and autonomous driving—require verifiable uncertainty robustness, yet existing probabilistic verification methods suffer from excessive conservatism and poor scalability in high-dimensional output spaces. To address this, we propose the first architecture-agnostic and scalable probabilistic robustness analysis framework for SSNs, integrating sampling-based reachability analysis with conformal inference. A key innovation is our high-dimensional conformal quantile calibration strategy, which significantly tightens verification bounds while preserving theoretical soundness. Evaluated on CamVid, Cityscapes, OCTA-500, and a lung segmentation dataset, our method improves verified accuracy over state-of-the-art by 12.7%–28.3%, delivering tighter and more practically usable safety guarantees. We publicly release a complete, open-source toolkit.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Semantic segmentation networks (SSNs) play a critical role in domains such as medical imaging, autonomous driving, and environmental monitoring, where safety hinges on reliable model behavior under uncertainty. Yet, existing probabilistic verification approaches struggle to scale with the complexity and dimensionality of modern segmentation tasks, often yielding guarantees that are too conservative to be practical. We introduce a probabilistic verification framework that is both architecture-agnostic and scalable to high-dimensional outputs. Our approach combines sampling-based reachability analysis with conformal inference (CI) to deliver provable guarantees while avoiding the excessive conservatism of prior methods. To counteract CI's limitations in high-dimensional settings, we propose novel strategies that reduce conservatism without compromising rigor. Empirical evaluation on large-scale segmentation models across CamVid, OCTA-500, Lung Segmentation, and Cityscapes demonstrates that our method provides reliable safety guarantees while substantially tightening bounds compared to SOTA. We also provide a toolbox implementing this technique, available on Github.

Problem

• Scalable probabilistic verification for high-dimensional semantic segmentation networks
• Overcoming conservatism in existing probabilistic robustness analysis methods
• Providing reliable safety guarantees for segmentation models under uncertainty

Innovation

• Sampling-based reachability analysis with conformal inference
• Architecture-agnostic probabilistic verification framework
• Novel strategies reducing conservatism in high dimensions