# VerifIoU -- Robustness of Object Detection to Perturbations

arXiv: https://arxiv.org/abs/2403.08788 · HTML: https://mlsanity.com/paper/2403.08788/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks](https://mlsanity.com/paper/1702.01135/) (2017)
- [On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models](https://mlsanity.com/paper/1810.12715/) (2018)
- [Safety Verification of Deep Neural Networks](https://mlsanity.com/paper/1610.06940/) (2016)
- [Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks](https://mlsanity.com/paper/1705.01320/) (2017)
- [Towards Stable and Efficient Training of Verifiably Robust Neural Networks](https://mlsanity.com/paper/1906.06316/) (2019)
- [Measuring Neural Net Robustness with Constraints](https://mlsanity.com/paper/1605.07262/) (2016)
- [Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds](https://mlsanity.com/paper/2111.01395/) (2021)
- [Scaling Polyhedral Neural Network Verification on GPUs](https://mlsanity.com/paper/2007.10868/) (2020)
- [A Review of Formal Methods applied to Machine Learning](https://mlsanity.com/paper/2104.02466/) (2021)
- [LARD -- Landing Approach Runway Detection -- Dataset for Vision Based Landing](https://mlsanity.com/paper/2304.09938/) (2023)
- [TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems](https://mlsanity.com/paper/2004.04320/) (2020)

## Built on by (most cited first, 3 of 3)

- [IoUCert: Robustness Verification for Anchor-based Object Detectors](https://mlsanity.com/paper/2603.03043/) (2026)
- [LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection](https://mlsanity.com/paper/2607.06592/) (2026)
- [Certified Training for Convolutional Perturbations](https://mlsanity.com/paper/2607.18195/) (2026)

## Abstract (arXiv)

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.