@inproceedings{f7ca1952e12d45f38d8c401719907f17,
title = "Steganalysis using high-dimensional features derived from co-occurrence matrix and class-wise non-principal components analysis (CNPCA)",
abstract = "This paper presents a novel steganalysis scheme with high-dimensional feature vectors derived from co-occurrence matrix in either spatial domain or JPEG coefficient domain, which is sensitive to data embedding process. The class-wise non-principal components analysis (CNPCA) is proposed to solve the problem of the classification in the high-dimensional feature vector space. The experimental results have demonstrated that the proposed scheme outperforms the existing steganalysis techniques in attacking the commonly used steganographic schemes applied to spatial domain (Spread-Spectrum, LSB, QIM) or JPEG domain (OutGuess, F5, Model-Based).",
keywords = "Class-wise non-principal components analysis (CNPCA), Co-occurrence matrix, Steganalysis",
author = "Guorong Xuan and Shi, {Yun Q.} and Cong Huang and Dongdong Fu and Xiuming Zhu and Peiqi Chai and Jianjiong Gao",
year = "2006",
doi = "10.1007/11922841_5",
language = "English (US)",
isbn = "3540488251",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "49--60",
booktitle = "Digital Watermarking - 5th International Workshop, IWDW 2006, Proceedings",
address = "Germany",
note = "5th International Workshop on Digital Watermarking, IWDW 2006 ; Conference date: 08-11-2006 Through 10-11-2006",
}