# A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability

arXiv: https://arxiv.org/abs/1812.08342 · HTML: https://mlsanity.com/paper/1812.08342/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2018.

## Built on by (most cited first, 8 of 8)

- [Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Voting](https://mlsanity.com/paper/2101.11466/) (2021)
- [Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation](https://mlsanity.com/paper/2112.10593/) (2021)
- [Bisimulations for Neural Network Reduction](https://mlsanity.com/paper/2110.03726/) (2021)
- [Zero-shot Safety Prediction for Autonomous Robots with Foundation World Models](https://mlsanity.com/paper/2404.00462/) (2024)
- [WeedNet: A Foundation Model-Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification](https://mlsanity.com/paper/2505.18930/) (2025)
- [CEG4N: Counter-Example Guided Neural Network Quantization Refinement](https://mlsanity.com/paper/2207.04231/) (2022)
- [Feature compression is the root cause of adversarial fragility in neural network classifiers](https://mlsanity.com/paper/2406.16200/) (2024)
- [A constrained optimization approach to improve robustness of neural networks](https://mlsanity.com/paper/2409.13770/) (2024)

## Abstract (arXiv)

In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks. With the broader deployment of DNNs on various applications, the concerns over their safety and trustworthiness have been raised in public, especially after the widely reported fatal incidents involving self-driving cars. Research to address these concerns is particularly active, with a significant number of papers released in the past few years. This survey paper conducts a review of the current research effort into making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability. In total, we survey 202 papers, most of which were published after 2017.