Scaling Data-Driven Probabilistic Robustness Analysis for Semantic Segmentation Neural Networks
Navid Hashemi, Samuel Sasaki, Ipek Oguz, Meiyi Ma, Taylor Johnson
Semantic segmentation neural networks (SSNs) are increasingly essential in high-stakes fields such as medical imaging, autonomous driving, and environmental monitoring, where robustness to input uncertainties and adversarial examples is crucial for ensuring safety and reliability. However, traditional probabilistic verification methods struggle to scale effectively with the size and depth of modern SSNs, especially when dealing with their high-dimensional, structured inputs/outputs. As the output dimension increases, these methods tend to become overly conservative, leading to pointless and overly restrictive safety guarantees. In this work, we propose a probabilistic, data-driven verification algorithm that is architecture-agnostic and scalable to handle the high-dimensional outputs of SSNs without introducing conservative and loose guarantees. We leverage efficient sampling-based reachability analysis to explore the space of possible outputs while maintaining computational feasibility. Our methodology is based on Conformal Inference (CI), which is known for its high data efficiency. However, CI tends to be overly conservative in high-dimensional spaces. To address this, in this paper, we introduce effective techniques that mitigate these sources of conservatism, enabling us to provide less conservative yet provable guarantees for SSNs. We validate our approach on deep segmentation models applied to benchmark datasets such as CamVid and Lung-Segmentation, showing that it can offer reliable safety guarantees while lowering the conservatism inherent in traditional methods.