# SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation

arXiv: https://arxiv.org/abs/2005.09296 · HTML: https://mlsanity.com/paper/2005.09296/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 15 of 15)

- [When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study](https://mlsanity.com/paper/2212.11368/) (2022)
- [Hierarchical Distribution-Aware Testing of Deep Learning](https://mlsanity.com/paper/2205.08589/) (2022)
- [Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing](https://mlsanity.com/paper/2207.10495/) (2022)
- [Validating Multimedia Content Moderation Software via Semantic Fusion](https://mlsanity.com/paper/2305.13623/) (2023)
- [Benchmarking Generative AI Models for Deep Learning Test Input Generation](https://mlsanity.com/paper/2412.17652/) (2024)
- [Targeted Deep Learning System Boundary Testing](https://mlsanity.com/paper/2408.06258/) (2024)
- [RBT4DNN: Requirements-based Testing of Neural Networks](https://mlsanity.com/paper/2504.02737/) (2025)
- [Provably Valid and Diverse Mutations of Real-World Media Data for DNN Testing](https://mlsanity.com/paper/2112.01956/) (2021)
- [DANDI: Diffusion as Normative Distribution for Deep Neural Network Input](https://mlsanity.com/paper/2502.02910/) (2025)
- [Towards Assessing Deep Learning Test Input Generators](https://mlsanity.com/paper/2504.02329/) (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)
- [Latent Anchor-Driven Test Generation for Deep Neural Networks](https://mlsanity.com/paper/2606.04310/) (2026)
- [Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes](https://mlsanity.com/paper/2606.04314/) (2026)
- [Graphical-Probabilistic Modeling of Generative Flows in LLM-Native Software Systems](https://mlsanity.com/paper/2606.15943/) (2026)

## Abstract (arXiv)

The testing of Deep Neural Networks (DNNs) has become increasingly important as DNNs are widely adopted by safety critical systems. While many test adequacy criteria have been suggested, automated test input generation for many types of DNNs remains a challenge because the raw input space is too large to randomly sample or to navigate and search for plausible inputs. Consequently, current testing techniques for DNNs depend on small local perturbations to existing inputs, based on the metamorphic testing principle. We propose new ways to search not over the entire image space, but rather over a plausible input space that resembles the true training distribution. This space is constructed using Variational Autoencoders (VAEs), and navigated through their latent vector space. We show that this space helps efficiently produce test inputs that can reveal information about the robustness of DNNs when dealing with realistic tests, opening the field to meaningful exploration through the space of highly structured images.