The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam makalah ini, kami mencadangkan algoritma baru yang dipanggil pengelompokan diskriminasi ensemble berbilang unjuran (MPEDC) untuk steganalisis JPEG. Skim ini menggunakan unjuran optimum algoritma analisis diskriminasi linear (LDA) untuk mendapatkan lebih banyak vektor unjuran dengan menggunakan kaedah putaran mikro. Vektor ini serupa dengan vektor optimum. MPEDC menggabungkan algoritma K-means tanpa pengawasan untuk membuat klasifikasi keputusan yang komprehensif secara adaptif. Kuasa kaedah yang dicadangkan ditunjukkan pada tiga kaedah steganografi dengan tiga kaedah pengekstrakan ciri. Keputusan eksperimen menunjukkan bahawa ketepatan boleh dipertingkatkan menggunakan pengelasan diskriminasi berulang.
Yan SUN
Shanghai University
Guorui FENG
Shanghai University
Yanli REN
Shanghai University
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Salinan
Yan SUN, Guorui FENG, Yanli REN, "JPEG Steganalysis Based on Multi-Projection Ensemble Discriminant Clustering" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 1, pp. 198-201, January 2019, doi: 10.1587/transinf.2018EDL8073.
Abstract: In this paper, we propose a novel algorithm called multi-projection ensemble discriminant clustering (MPEDC) for JPEG steganalysis. The scheme makes use of the optimal projection of linear discriminant analysis (LDA) algorithm to get more projection vectors by using the micro-rotation method. These vectors are similar to the optimal vector. MPEDC combines unsupervised K-means algorithm to make a comprehensive decision classification adaptively. The power of the proposed method is demonstrated on three steganographic methods with three feature extraction methods. Experimental results show that the accuracy can be improved using iterative discriminant classification.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8073/_p
Salinan
@ARTICLE{e102-d_1_198,
author={Yan SUN, Guorui FENG, Yanli REN, },
journal={IEICE TRANSACTIONS on Information},
title={JPEG Steganalysis Based on Multi-Projection Ensemble Discriminant Clustering},
year={2019},
volume={E102-D},
number={1},
pages={198-201},
abstract={In this paper, we propose a novel algorithm called multi-projection ensemble discriminant clustering (MPEDC) for JPEG steganalysis. The scheme makes use of the optimal projection of linear discriminant analysis (LDA) algorithm to get more projection vectors by using the micro-rotation method. These vectors are similar to the optimal vector. MPEDC combines unsupervised K-means algorithm to make a comprehensive decision classification adaptively. The power of the proposed method is demonstrated on three steganographic methods with three feature extraction methods. Experimental results show that the accuracy can be improved using iterative discriminant classification.},
keywords={},
doi={10.1587/transinf.2018EDL8073},
ISSN={1745-1361},
month={January},}
Salinan
TY - JOUR
TI - JPEG Steganalysis Based on Multi-Projection Ensemble Discriminant Clustering
T2 - IEICE TRANSACTIONS on Information
SP - 198
EP - 201
AU - Yan SUN
AU - Guorui FENG
AU - Yanli REN
PY - 2019
DO - 10.1587/transinf.2018EDL8073
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2019
AB - In this paper, we propose a novel algorithm called multi-projection ensemble discriminant clustering (MPEDC) for JPEG steganalysis. The scheme makes use of the optimal projection of linear discriminant analysis (LDA) algorithm to get more projection vectors by using the micro-rotation method. These vectors are similar to the optimal vector. MPEDC combines unsupervised K-means algorithm to make a comprehensive decision classification adaptively. The power of the proposed method is demonstrated on three steganographic methods with three feature extraction methods. Experimental results show that the accuracy can be improved using iterative discriminant classification.
ER -