# Chromatic and spatial analysis of one-pixel attacks against an image classifier

arXiv: https://arxiv.org/abs/2105.13771 · HTML: https://mlsanity.com/paper/2105.13771/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 8 of 8)

- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [Secure and Robust Machine Learning for Healthcare: A Survey](https://mlsanity.com/paper/2001.08103/) (2020)
- [Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge](https://mlsanity.com/paper/1807.08284/) (2018)
- [Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems](https://mlsanity.com/paper/2010.03671/) (2020)
- [Understanding the One-Pixel Attack: Propagation Maps and Locality Analysis](https://mlsanity.com/paper/1902.02947/) (2019)
- [Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning](https://mlsanity.com/paper/2105.01959/) (2021)
- [One-Pixel Attack Deceives Computer-Assisted Diagnosis of Cancer](https://mlsanity.com/paper/2012.00517/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Review of One-Pixel Attack: Research Status, Taxonomy, Applications, Regulation Policy and Future Directions](https://mlsanity.com/paper/2610.00125/) (2026)

## Abstract (arXiv)

One-pixel attack is a curious way of deceiving neural network classifier by changing only one pixel in the input image. The full potential and boundaries of this attack method are not yet fully understood. In this research, the successful and unsuccessful attacks are studied in more detail to illustrate the working mechanisms of a one-pixel attack created using differential evolution. The data comes from our earlier studies where we applied the attack against medical imaging. We used a real breast cancer tissue dataset and a real classifier as the attack target. This research presents ways to analyze chromatic and spatial distributions of one-pixel attacks. In addition, we present one-pixel attack confidence maps to illustrate the behavior of the target classifier. We show that the more effective attacks change the color of the pixel more, and that the successful attacks are situated at the center of the images. This kind of analysis is not only useful for understanding the behavior of the attack but also the qualities of the classifying neural network.