# ModelGuard: Runtime Validation of Lipschitz-continuous Models

arXiv: https://arxiv.org/abs/2104.15006 · HTML: https://mlsanity.com/paper/2104.15006/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 30 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Regularisation of Neural Networks by Enforcing Lipschitz Continuity](https://mlsanity.com/paper/1804.04368/) (2018)
- [Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks](https://mlsanity.com/paper/1906.04893/) (2019)
- [Verisig: verifying safety properties of hybrid systems with neural network controllers](https://mlsanity.com/paper/1811.01828/) (2018)
- [Scenic: A Language for Scenario Specification and Scene Generation](https://mlsanity.com/paper/1809.09310/) (2018)
- [Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controller](https://mlsanity.com/paper/1910.11309/) (2019)
- [Data-Driven Model Invalidation for Unknown Lipschitz Continuous Systems via Abstraction](https://mlsanity.com/paper/2003.11662/) (2020)

## Built on by (most cited first, 1 of 1)

- [Confidence Composition for Monitors of Verification Assumptions](https://mlsanity.com/paper/2111.03782/) (2021)

## Abstract (arXiv)

This paper presents ModelGuard, a sampling-based approach to runtime model validation for Lipschitz-continuous models. Although techniques exist for the validation of many classes of models the majority of these methods cannot be applied to the whole of Lipschitz-continuous models, which includes neural network models. Additionally, existing techniques generally consider only white-box models. By taking a sampling-based approach, we can address black-box models, represented only by an input-output relationship and a Lipschitz constant. We show that by randomly sampling from a parameter space and evaluating the model, it is possible to guarantee the correctness of traces labeled consistent and provide a confidence on the correctness of traces labeled inconsistent. We evaluate the applicability and scalability of ModelGuard in three case studies, including a physical platform.