# Detecting Adversarial Examples through Nonlinear Dimensionality Reduction

arXiv: https://arxiv.org/abs/1904.13094 · HTML: https://mlsanity.com/paper/1904.13094/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Detection of Adversarial Physical Attacks in Time-Series Image Data](https://mlsanity.com/paper/2304.13919/) (2023)
- [Understanding Adversarial Examples Through Deep Neural Network's Response Surface and Uncertainty Regions](https://mlsanity.com/paper/2107.00003/) (2021)
- [Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples](https://mlsanity.com/paper/2107.00561/) (2021)

## Abstract (arXiv)

Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combining non-linear dimensionality reduction and density estimation techniques. Our empirical findings show that the proposed approach is able to effectively detect adversarial examples crafted by non-adaptive attackers, i.e., not specifically tuned to bypass the detection method. Given our promising results, we plan to extend our analysis to adaptive attackers in future work.