# IoUCert: Robustness Verification for Anchor-based Object Detectors Source: [IoUCert: Robustness Verification for Anchor-based Object Detectors]() Collection index: [llms.txt]() Data updated: 2026-07-22T05:00:21.499Z Authors: Benedikt Br\\"uckner, Alejandro J. Mercado, Yanghao Zhang, Panagiotis Kouvaros, Alessio Lomuscio Published: 2026-03-03T14:36:46.000Z ## Abstract While formal robustness verification has seen significant success in image classification, scaling these guarantees to object detection remains notoriously difficult due to complex non-linear coordinate transformations and Intersection-over-Union (IoU) metrics. As a fundamental step towards verifying complete detection pipelines, we introduce IoUCert, a novel formal verification framework designed specifically to overcome these core mathematical bottlenecks. By isolating the object localisation task in single-object settings, we propose a coordinate transformation that circumvents precision-degrading relaxations of non-linear box prediction functions. This approach allows us to optimise bounds directly with respect to anchor box offsets, enabling a novel Interval Bound Propagation method that derives optimal IoU bounds. We demonstrate that IoUCert enables, for the first time, the robustness verification of foundational, anchor-based architectures including tractable variants of SSD, YOLOv2, and YOLOv3 against various input perturbations, providing a rigorous theoretical basis for future end-to-end detector verification. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()