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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo, propomos um novo algoritmo denominado cluster discriminante de conjunto multiprojeção (MPEDC) para esteganálise JPEG. O esquema faz uso da projeção ideal do algoritmo de análise discriminante linear (LDA) para obter mais vetores de projeção usando o método de micro-rotação. Esses vetores são semelhantes ao vetor ideal. MPEDC combina algoritmo K-means não supervisionado para fazer uma classificação de decisão abrangente de forma adaptativa. O poder do método proposto é demonstrado em três métodos esteganográficos com três métodos de extração de características. Resultados experimentais mostram que a precisão pode ser melhorada usando classificação discriminante iterativa.
Yan SUN
Shanghai University
Guorui FENG
Shanghai University
Yanli REN
Shanghai University
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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
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@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},}
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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 -