Proceedings

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

Qingzhong Liu, Andrew H. Sung

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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.

Qingzhong Liu, and Andrew H. Sung. 2007. Feature Mining and Neuro-Fuzzy Inference System for Steganalysis of LSB Matching Stegangoraphy in Grayscale Images. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07. IJCAI, 2808–2812. https://ijcai.org/proceedings/2007/451
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