# Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing

arXiv: https://arxiv.org/abs/2402.00035 · HTML: https://mlsanity.com/paper/2402.00035/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 8 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Safety Verification of Deep Neural Networks](https://mlsanity.com/paper/1610.06940/) (2016)
- [PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations](https://mlsanity.com/paper/2103.03638/) (2021)
- [An Abstraction-Based Framework for Neural Network Verification](https://mlsanity.com/paper/1910.14574/) (2019)
- [First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)](https://mlsanity.com/paper/2301.05815/) (2023)
- [Fastened CROWN: Tightened Neural Network Robustness Certificates](https://mlsanity.com/paper/1912.00574/) (2019)
- [Robustness of Neural Networks: A Probabilistic and Practical Approach](https://mlsanity.com/paper/1902.05983/) (2019)
- [Neural Network Robustness as a Verification Property: A Principled Case Study](https://mlsanity.com/paper/2104.01396/) (2021)
- [An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks](https://mlsanity.com/paper/2201.01978/) (2022)
- [Efficient Neural Network Analysis with Sum-of-Infeasibilities](https://mlsanity.com/paper/2203.11201/) (2022)
- [Neural Network Verification with Proof Production](https://mlsanity.com/paper/2206.00512/) (2022)
- [Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives](https://mlsanity.com/paper/1709.00911/) (2017)

## Built on by (most cited first, 8 of 8)

- [A Certified Proof Checker for Deep Neural Network Verification in Imandra](https://mlsanity.com/paper/2405.10611/) (2024)
- [Towards Formal Verification of Deep Neural Networks for Object Detection](https://mlsanity.com/paper/2407.01295/) (2024)
- [Proof Minimization in Neural Network Verification](https://mlsanity.com/paper/2511.08198/) (2025)
- [PICID: Proof-Driven Clause Learning in Neural Network Verification](https://mlsanity.com/paper/2503.12083/) (2025)
- [Incremental Neural Network Verification via Learned Conflicts](https://mlsanity.com/paper/2603.12232/) (2026)
- [Implementation of airborne ML models with semantics preservation](https://mlsanity.com/paper/2509.18681/) (2025)
- [On Improving Deep Active Learning with Formal Verification](https://mlsanity.com/paper/2512.14170/) (2025)
- [Talking with Verifiers: Automatic Specification Generation for Neural Network Verification](https://mlsanity.com/paper/2603.02235/) (2026)

## Abstract (arXiv)

As deep neural networks (DNNs) are becoming the prominent solution for many computational problems, the aviation industry seeks to explore their potential in alleviating pilot workload and in improving operational safety. However, the use of DNNs in this type of safety-critical applications requires a thorough certification process. This need can be addressed through formal verification, which provides rigorous assurances -- e.g.,~by proving the absence of certain mispredictions. In this case-study paper, we demonstrate this process using an image-classifier DNN currently under development at Airbus and intended for use during the aircraft taxiing phase. We use formal methods to assess this DNN's robustness to three common image perturbation types: noise, brightness and contrast, and some of their combinations. This process entails multiple invocations of the underlying verifier, which might be computationally expensive; and we therefore propose a method that leverages the monotonicity of these robustness properties, as well as the results of past verification queries, in order to reduce the overall number of verification queries required by nearly 60%. Our results provide an indication of the level of robustness achieved by the DNN classifier under study, and indicate that it is considerably more vulnerable to noise than to brightness or contrast perturbations.