# Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

arXiv: https://arxiv.org/abs/1907.07296 · HTML: https://mlsanity.com/paper/1907.07296/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

## Built on by (most cited first, 24 of 24)

- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [Poisoning Attacks and Defenses on Artificial Intelligence: A Survey](https://mlsanity.com/paper/2202.10276/) (2022)
- [Class-constrained t-SNE: Combining Data Features and Class Probabilities](https://mlsanity.com/paper/2308.13837/) (2023)
- [FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches](https://mlsanity.com/paper/2103.14539/) (2021)
- [JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Models](https://mlsanity.com/paper/2404.08793/) (2024)
- [Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles](https://mlsanity.com/paper/2103.10229/) (2021)
- [Interactive Machine Learning: A State of the Art Review](https://mlsanity.com/paper/2207.06196/) (2022)
- [A Review of Explainable Artificial Intelligence in Manufacturing](https://mlsanity.com/paper/2107.02295/) (2021)
- [HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques](https://mlsanity.com/paper/2203.15753/) (2022)
- [Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks](https://mlsanity.com/paper/2303.02814/) (2023)
- [New data poison attacks on machine learning classifiers for mobile exfiltration](https://mlsanity.com/paper/2210.11592/) (2022)
- [DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps](https://mlsanity.com/paper/2304.00133/) (2023)
- [Panda or not Panda? Understanding Adversarial Attacks with Interactive Visualization](https://mlsanity.com/paper/2311.13656/) (2023)
- [When AI Defeats Password Deception! A Deep Learning Framework to Distinguish Passwords and Honeywords](https://mlsanity.com/paper/2407.16964/) (2024)
- [STARdom: an architecture for trusted and secure human-centered manufacturing systems](https://mlsanity.com/paper/2104.00983/) (2021)
- [MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels](https://mlsanity.com/paper/2212.03539/) (2022)
- [TRIVEA: Transparent Ranking Interpretation using Visual Explanation of Black-Box Algorithmic Rankers](https://mlsanity.com/paper/2308.14622/) (2023)
- [TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic](https://mlsanity.com/paper/2403.04812/) (2024)
- [NeuroBreak: Unveil Internal Jailbreak Mechanisms in Large Language Models](https://mlsanity.com/paper/2509.03985/) (2025)
- [VisMoDAl: Visual Analytics for Evaluating and Improving Corruption Robustness of Vision-Language Models](https://mlsanity.com/paper/2509.14571/) (2025)
- [LINGO : Visually Debiasing Natural Language Instructions to Support Task Diversity](https://mlsanity.com/paper/2304.06184/) (2023)
- [Exploring the Lands Between: A Method for Finding Differences between AI-Decisions and Human Ratings through Generated Samples](https://mlsanity.com/paper/2409.12801/) (2024)
- [SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions](https://mlsanity.com/paper/2605.12792/) (2026)

## Abstract (arXiv)

Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and numerous other critical tasks. As the deployment of artificial intelligence technologies becomes ubiquitous, it is unsurprising that adversaries have begun developing methods to manipulate machine learning models to their advantage. While the visual analytics community has developed methods for opening the black box of machine learning models, little work has focused on helping the user understand their model vulnerabilities in the context of adversarial attacks. In this paper, we present a visual analytics framework for explaining and exploring model vulnerabilities to adversarial attacks. Our framework employs a multi-faceted visualization scheme designed to support the analysis of data poisoning attacks from the perspective of models, data instances, features, and local structures. We demonstrate our framework through two case studies on binary classifiers and illustrate model vulnerabilities with respect to varying attack strategies.