---
title: "Feature Mining and Neuro-Fuzzy Inference System for Steganalysis of LSB Matching Stegangoraphy in Grayscale Images"
authors:
  - "Qingzhong Liu"
  - "Andrew H. Sung"
doi: ""
url: "https://ijcai.org/proceedings/2007/451"
pdf: "https://ijcai.org/proceedings/2007/0451.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "2808-2812"
track: ""
keywords:
---

# Feature Mining and Neuro-Fuzzy Inference System for Steganalysis of LSB Matching Stegangoraphy in Grayscale Images

Qingzhong Liu, Andrew H. Sung

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 2808-2812.

## Abstract

In this paper, we present a scheme based on feature mining and neuro-fuzzy inference system for detecting LSB matching steganography in grayscale images, which is a very challenging problem in steganalysis. Four types of features are proposed, and a Dynamic Evolving Neural Fuzzy Inference System (DENFIS) based feature selection is proposed, as well as the use of Support Vector Machine Recursive Feature Elimination (SVM-RFE) to obtain better detection accuracy. In comparison with other well-known features, overall, our features perform the best. DENFIS outperforms some traditional learning classifiers. SVM-RFE and DENFIS based feature selection outperform statistical significance based feature selection such as t-test. Experimental results also indicate that it remains very challenging to steganalyze LSB matching steganography in grayscale images with high complexity.