# VERIFAI: A Toolkit for the Design and Analysis of Artificial Intelligence-Based Systems

arXiv: https://arxiv.org/abs/1902.04245 · HTML: https://mlsanity.com/paper/1902.04245/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2019.

## Built on by (most cited first, 6 of 6)

- [Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles](https://mlsanity.com/paper/2109.06126/) (2021)
- [Distributionally Robust Statistical Verification with Imprecise Neural Networks](https://mlsanity.com/paper/2308.14815/) (2023)
- [Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification](https://mlsanity.com/paper/2205.10906/) (2022)
- [Checking Trustworthiness of Probabilistic Computations in a Typed Natural Deduction System](https://mlsanity.com/paper/2206.12934/) (2022)
- [Exploring Effects of Computational Parameter Changes to Image Recognition Systems](https://mlsanity.com/paper/2211.00471/) (2022)
- [DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models](https://mlsanity.com/paper/2306.06208/) (2023)

## Abstract (arXiv)

We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components. VERIFAI particularly seeks to address challenges with applying formal methods to perception and ML components, including those based on neural networks, and to model and analyze system behavior in the presence of environment uncertainty. We describe the initial version of VERIFAI which centers on simulation guided by formal models and specifications. Several use cases are illustrated with examples, including temporal-logic falsification, model-based systematic fuzz testing, parameter synthesis, counterexample analysis, and data set augmentation.