# Towards Generating Adversarial Examples on Mixed-type Data

arXiv: https://arxiv.org/abs/2210.09405 · HTML: https://mlsanity.com/paper/2210.09405/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 18 of 18)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks](https://mlsanity.com/paper/1804.00792/) (2018)
- [Do Deep Generative Models Know What They Don't Know?](https://mlsanity.com/paper/1810.09136/) (2018)
- [Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers](https://mlsanity.com/paper/1801.04354/) (2018)
- [Generating Adversarial Examples with Adversarial Networks](https://mlsanity.com/paper/1801.02610/) (2018)
- [Adversarial Training and Robustness for Multiple Perturbations](https://mlsanity.com/paper/1904.13000/) (2019)
- [Transferable Adversarial Attacks for Image and Video Object Detection](https://mlsanity.com/paper/1811.12641/) (2018)
- [Adversarial Robustness Against the Union of Multiple Perturbation Models](https://mlsanity.com/paper/1909.04068/) (2019)
- [Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data](https://mlsanity.com/paper/1805.12316/) (2018)
- [Adversarial Robustness with Non-uniform Perturbations](https://mlsanity.com/paper/2102.12002/) (2021)
- [Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis](https://mlsanity.com/paper/2107.04882/) (2021)

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

- [On the Efficiency of Training Robust Decision Trees](https://mlsanity.com/paper/2507.10048/) (2025)

## Abstract (arXiv)

The existence of adversarial attacks (or adversarial examples) brings huge concern about the machine learning (ML) model's safety issues. For many safety-critical ML tasks, such as financial forecasting, fraudulent detection, and anomaly detection, the data samples are usually mixed-type, which contain plenty of numerical and categorical features at the same time. However, how to generate adversarial examples with mixed-type data is still seldom studied. In this paper, we propose a novel attack algorithm M-Attack, which can effectively generate adversarial examples in mixed-type data. Based on M-Attack, attackers can attempt to mislead the targeted classification model's prediction, by only slightly perturbing both the numerical and categorical features in the given data samples. More importantly, by adding designed regularizations, our generated adversarial examples can evade potential detection models, which makes the attack indeed insidious. Through extensive empirical studies, we validate the effectiveness and efficiency of our attack method and evaluate the robustness of existing classification models against our proposed attack. The experimental results highlight the feasibility of generating adversarial examples toward machine learning models in real-world applications.