# Analyzing the Impact of Adversarial Examples on Explainable Machine Learning

arXiv: https://arxiv.org/abs/2307.08327 · HTML: https://mlsanity.com/paper/2307.08327/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 17 of 17)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://mlsanity.com/paper/1911.02508/) (2019)
- [Adversarial Examples Improve Image Recognition](https://mlsanity.com/paper/1911.09665/) (2019)
- [Adversarial Attacks and Defenses in Images, Graphs and Text: A Review](https://mlsanity.com/paper/1909.08072/) (2019)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [OpenAttack: An Open-source Textual Adversarial Attack Toolkit](https://mlsanity.com/paper/2009.09191/) (2020)
- [AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models](https://mlsanity.com/paper/1909.09251/) (2019)
- [On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models](https://mlsanity.com/paper/1903.06620/) (2019)
- [Towards Crafting Text Adversarial Samples](https://mlsanity.com/paper/1707.02812/) (2017)
- [A Survey on Adversarial Attacks for Malware Analysis](https://mlsanity.com/paper/2111.08223/) (2021)
- [Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability](https://mlsanity.com/paper/2009.13243/) (2020)
- [Evading classifiers in discrete domains with provable optimality guarantees](https://mlsanity.com/paper/1810.10939/) (2018)
- [Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning](https://mlsanity.com/paper/2105.01959/) (2021)

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

- [GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models](https://mlsanity.com/paper/2505.10983/) (2025)

## Abstract (arXiv)

Adversarial attacks are a type of attack on machine learning models where an attacker deliberately modifies the inputs to cause the model to make incorrect predictions. Adversarial attacks can have serious consequences, particularly in applications such as autonomous vehicles, medical diagnosis, and security systems. Work on the vulnerability of deep learning models to adversarial attacks has shown that it is very easy to make samples that make a model predict things that it doesn't want to. In this work, we analyze the impact of model interpretability due to adversarial attacks on text classification problems. We develop an ML-based classification model for text data. Then, we introduce the adversarial perturbations on the text data to understand the classification performance after the attack. Subsequently, we analyze and interpret the model's explainability before and after the attack