# Benchmarking Generative AI Models for Deep Learning Test Input Generation

arXiv: https://arxiv.org/abs/2412.17652 · HTML: https://mlsanity.com/paper/2412.17652/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 31 of its 81 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 31)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems](https://mlsanity.com/paper/1803.07519/) (2018)
- [Taxonomy of Real Faults in Deep Learning Systems](https://mlsanity.com/paper/1910.11015/) (2019)
- [DLFuzz: Differential Fuzzing Testing of Deep Learning Systems](https://mlsanity.com/paper/1808.09413/) (2018)
- [Misbehaviour Prediction for Autonomous Driving Systems](https://mlsanity.com/paper/1910.04443/) (2019)
- [On Testing Machine Learning Programs](https://mlsanity.com/paper/1812.02257/) (2018)
- [Model-based Exploration of the Frontier of Behaviours for Deep Learning System Testing](https://mlsanity.com/paper/2007.02787/) (2020)
- [A Survey on Automated Driving System Testing: Landscapes and Trends](https://mlsanity.com/paper/2206.05961/) (2022)
- [DeepHyperion: Exploring the Feature Space of Deep Learning-Based Systems through Illumination Search](https://mlsanity.com/paper/2107.06997/) (2021)
- [Distribution-Aware Testing of Neural Networks Using Generative Models](https://mlsanity.com/paper/2102.13602/) (2021)
- [Testing of Autonomous Driving Systems: Where Are We and Where Should We Go?](https://mlsanity.com/paper/2106.12233/) (2021)
- [When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study](https://mlsanity.com/paper/2212.11368/) (2022)
- [DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks](https://mlsanity.com/paper/1909.02563/) (2019)
- [SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation](https://mlsanity.com/paper/2005.09296/) (2020)
- [DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score](https://mlsanity.com/paper/2109.07514/) (2021)
- [Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning](https://mlsanity.com/paper/2002.00863/) (2020)
- [Exposing Previously Undetectable Faults in Deep Neural Networks](https://mlsanity.com/paper/2106.00576/) (2021)
- [Identifying and Explaining Safety-critical Scenarios for Autonomous Vehicles via Key Features](https://mlsanity.com/paper/2212.07566/) (2022)
- [Operation is the hardest teacher: estimating DNN accuracy looking for mispredictions](https://mlsanity.com/paper/2102.04287/) (2021)
- [Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering](https://mlsanity.com/paper/2201.05077/) (2022)
- [Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing](https://mlsanity.com/paper/2207.10495/) (2022)
- [DeepSmartFuzzer: Reward Guided Test Generation For Deep Learning](https://mlsanity.com/paper/1911.10621/) (2019)

## Built on by (most cited first, 4 of 4)

- [Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems](https://mlsanity.com/paper/2501.12269/) (2025)
- [HyperNet-Adaptation for Diffusion-Based Test Case Generation](https://mlsanity.com/paper/2601.15041/) (2026)
- [Latent Regularization in Generative Test Input Generation](https://mlsanity.com/paper/2602.15552/) (2026)
- [Generative Testing of Automated Speech Recognition Systems](https://mlsanity.com/paper/2607.09833/) (2026)

## Abstract (arXiv)

Test Input Generators (TIGs) are crucial to assess the ability of Deep Learning (DL) image classifiers to provide correct predictions for inputs beyond their training and test sets. Recent advancements in Generative AI (GenAI) models have made them a powerful tool for creating and manipulating synthetic images, although these advancements also imply increased complexity and resource demands for training. In this work, we benchmark and combine different GenAI models with TIGs, assessing their effectiveness, efficiency, and quality of the generated test images, in terms of domain validity and label preservation. We conduct an empirical study involving three different GenAI architectures (VAEs, GANs, Diffusion Models), five classification tasks of increasing complexity, and 364 human evaluations. Our results show that simpler architectures, such as VAEs, are sufficient for less complex datasets like MNIST. However, when dealing with feature-rich datasets, such as ImageNet, more sophisticated architectures like Diffusion Models achieve superior performance by generating a higher number of valid, misclassification-inducing inputs.