# Chaotic Variational Auto encoder-based Adversarial Machine Learning

arXiv: https://arxiv.org/abs/2302.12959 · HTML: https://mlsanity.com/paper/2302.12959/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 19 of 19)

- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Adversarial Attacks and Defences: A Survey](https://mlsanity.com/paper/1810.00069/) (2018)
- [Generating Adversarial Examples with Adversarial Networks](https://mlsanity.com/paper/1801.02610/) (2018)
- [Adversarial examples for generative models](https://mlsanity.com/paper/1702.06832/) (2017)
- [Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data](https://mlsanity.com/paper/2101.08030/) (2021)
- [PuVAE: A Variational Autoencoder to Purify Adversarial Examples](https://mlsanity.com/paper/1903.00585/) (2019)
- [Adversarial Images for Variational Autoencoders](https://mlsanity.com/paper/1612.00155/) (2016)
- [Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Models](https://mlsanity.com/paper/1910.06838/) (2019)
- [Towards a Theoretical Understanding of the Robustness of Variational Autoencoders](https://mlsanity.com/paper/2007.07365/) (2020)
- [A Survey on Adversarial Attacks for Malware Analysis](https://mlsanity.com/paper/2111.08223/) (2021)
- [Defense-VAE: A Fast and Accurate Defense against Adversarial Attacks](https://mlsanity.com/paper/1812.06570/) (2018)
- [Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE](https://mlsanity.com/paper/2108.11032/) (2021)
- [Adversarial Attack via Dual-Stage Network Erosion](https://mlsanity.com/paper/2201.00097/) (2022)
- [Feature subset selection for Big Data via Chaotic Binary Differential Evolution under Apache Spark](https://mlsanity.com/paper/2202.03795/) (2022)
- [Parallel and Streaming Wavelet Neural Networks for Classification and Regression under Apache Spark](https://mlsanity.com/paper/2209.03056/) (2022)
- [Chaotic Variational Auto Encoder based One Class Classifier for Insurance Fraud Detection](https://mlsanity.com/paper/2212.07802/) (2022)
- [Scalable Feature Subset Selection for Big Data using Parallel Hybrid Evolutionary Algorithm based Wrapper in Apache Spark](https://mlsanity.com/paper/2106.14007/) (2021)

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

- [Bi-Level Chaotic Fusion Based Graph Convolutional Network for Stock Market Prediction Interval](https://mlsanity.com/paper/2605.16324/) (2026)

## Abstract (arXiv)

Machine Learning (ML) has become the new contrivance in almost every field. This makes them a target of fraudsters by various adversary attacks, thereby hindering the performance of ML models. Evasion and Data-Poison-based attacks are well acclaimed, especially in finance, healthcare, etc. This motivated us to propose a novel computationally less expensive attack mechanism based on the adversarial sample generation by Variational Auto Encoder (VAE). It is well known that Wavelet Neural Network (WNN) is considered computationally efficient in solving image and audio processing, speech recognition, and time-series forecasting. This paper proposed VAE-Deep-Wavelet Neural Network (VAE-Deep-WNN), where Encoder and Decoder employ WNN networks. Further, we proposed chaotic variants of both VAE with Multi-layer perceptron (MLP) and Deep-WNN and named them C-VAE-MLP and C-VAE-Deep-WNN, respectively. Here, we employed a Logistic map to generate random noise in the latent space. In this paper, we performed VAE-based adversary sample generation and applied it to various problems related to finance and cybersecurity domain-related problems such as loan default, credit card fraud, and churn modelling, etc., We performed both Evasion and Data-Poison attacks on Logistic Regression (LR) and Decision Tree (DT) models. The results indicated that VAE-Deep-WNN outperformed the rest in the majority of the datasets and models. However, its chaotic variant C-VAE-Deep-WNN performed almost similarly to VAE-Deep-WNN in the majority of the datasets.