# DeepSmartFuzzer: Reward Guided Test Generation For Deep Learning

arXiv: https://arxiv.org/abs/1911.10621 · HTML: https://mlsanity.com/paper/1911.10621/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review](https://mlsanity.com/paper/2107.12045/) (2021)
- [ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection](https://mlsanity.com/paper/2106.08890/) (2021)
- [DeepHyperion: Exploring the Feature Space of Deep Learning-Based Systems through Illumination Search](https://mlsanity.com/paper/2107.06997/) (2021)
- [When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study](https://mlsanity.com/paper/2212.11368/) (2022)
- [Deep Intellectual Property Protection: A Survey](https://mlsanity.com/paper/2304.14613/) (2023)
- [Benchmarking Generative AI Models for Deep Learning Test Input Generation](https://mlsanity.com/paper/2412.17652/) (2024)
- [Provably Valid and Diverse Mutations of Real-World Media Data for DNN Testing](https://mlsanity.com/paper/2112.01956/) (2021)

## Abstract (arXiv)

Testing Deep Neural Network (DNN) models has become more important than ever with the increasing usage of DNN models in safety-critical domains such as autonomous cars. The traditional approach of testing DNNs is to create a test set, which is a random subset of the dataset about the problem of interest. This kind of approach is not enough for testing most of the real-world scenarios since these traditional test sets do not include corner cases, while a corner case input is generally considered to introduce erroneous behaviors. Recent works on adversarial input generation, data augmentation, and coverage-guided fuzzing (CGF) have provided new ways to extend traditional test sets. Among those, CGF aims to produce new test inputs by fuzzing existing ones to achieve high coverage on a test adequacy criterion (i.e. coverage criterion). Given that the subject test adequacy criterion is a well-established one, CGF can potentially find error inducing inputs for different underlying reasons. In this paper, we propose a novel CGF solution for structural testing of DNNs. The proposed fuzzer employs Monte Carlo Tree Search to drive the coverage-guided search in the pursuit of achieving high coverage. Our evaluation shows that the inputs generated by our method result in higher coverage than the inputs produced by the previously introduced coverage-guided fuzzing techniques.