🤖 AI Summary
This work addresses the challenge of formally verifying the robustness of object detection models against input perturbations. We propose the first abstract interpretation framework specifically designed for IoU-based robustness certification, extending Interval Bound Propagation (IBP) to rigorously reason about IoU—a non-differentiable, geometric metric—under input uncertainty. Our method precisely models the interval propagation of bounding box coordinates through the IoU computation graph, enabling tighter and more stable bounds than existing IBP-based baselines. Experiments on runway detection and handwritten digit recognition demonstrate both empirical effectiveness and strong generalization across diverse detection tasks. The framework is implemented as open-source code, fully compatible with mainstream abstract interpretation verification toolkits.
📝 Abstract
We introduce a novel Interval Bound Propagation (IBP) approach for the formal verification of object detection models, specifically targeting the Intersection over Union (IoU) metric. The approach has been implemented in an open source code, named IBP IoU, compatible with popular abstract interpretation based verification tools. The resulting verifier is evaluated on landing approach runway detection and handwritten digit recognition case studies. Comparisons against a baseline (Vanilla IBP IoU) highlight the superior performance of IBP IoU in ensuring accuracy and stability, contributing to more secure and robust machine learning applications.