# Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks

arXiv: https://arxiv.org/abs/2102.09695 · HTML: https://mlsanity.com/paper/2102.09695/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 19 Feb 2021.

## Builds on (most cited first, 11 of 11)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Adversarial Attacks and Defences: A Survey](https://mlsanity.com/paper/1810.00069/) (2018)
- [Adversarial Attacks and Defenses in Images, Graphs and Text: A Review](https://mlsanity.com/paper/1909.08072/) (2019)
- [On Detecting Adversarial Perturbations](https://mlsanity.com/paper/1702.04267/) (2017)
- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective](https://mlsanity.com/paper/2009.03728/) (2020)
- [Foolbox: A Python toolbox to benchmark the robustness of machine learning models](https://mlsanity.com/paper/1707.04131/) (2017)
- [Using Single-Step Adversarial Training to Defend Iterative Adversarial Examples](https://mlsanity.com/paper/2002.09632/) (2020)
- [Black Box to White Box: Discover Model Characteristics Based on Strategic Probing](https://mlsanity.com/paper/2009.03136/) (2020)

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

- [Automating Defense Against Adversarial Attacks: Discovery of Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed Models](https://mlsanity.com/paper/2103.15897/) (2021)

## Abstract (arXiv)

Production machine learning systems are consistently under attack by adversarial actors. Various deep learning models must be capable of accurately detecting fake or adversarial input while maintaining speed. In this work, we propose one piece of the production protection system: detecting an incoming adversarial attack and its characteristics. Detecting types of adversarial attacks has two primary effects: the underlying model can be trained in a structured manner to be robust from those attacks and the attacks can be potentially filtered out in real-time before causing any downstream damage. The adversarial image classification space is explored for models commonly used in transfer learning.