# VerifIoU -- Robustness of Object Detection to Perturbations

Source: [VerifIoU -- Robustness of Object Detection to Perturbations](<https://www.sota2.com/research/paper/68fa72851e209131cea56ff7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-01T05:00:42.467Z

Authors: No\\'emie Cohen, M\\'elanie Ducoffe, Ryma Boumazouza, Christophe Gabreau, Claire Pagetti, Xavier Pucel, Audrey Galametz

Published: 2024-01-30T09:05:38.000Z

## 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.

## 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](<https://arxiv.org/abs/2403.08788>)

[PDF](<https://arxiv.org/pdf/2403.08788.pdf>)