Steganalysis using high-dimensional features derived from co-occurrence matrix and class-wise non-principal components analysis (CNPCA)

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4283 LNCS, Issue , 2006, Pages 49-60

  Xuan, Guorong ^a   Shi, Yun Q ^b   Huang, Cong ^a   Fu, Dongdong ^b   Zhu, Xiuming ^a   Chai, Peiqi ^a   Gao, Jianjiong ^a  

^a TONGJI UNIVERSITY   (China)

^b NEW JERSEY INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Class wise non principal components analysis (CNPCA); Co occurrence matrix; Steganalysis

Indexed keywords

CLASSIFICATION (OF INFORMATION); IMAGE COMPRESSION; PRINCIPAL COMPONENT ANALYSIS; PROBLEM SOLVING; SPREAD SPECTRUM COMMUNICATION; VECTORS;

CLASS-WISE NON-PRINCIPAL COMPONENTS ANALYSIS (CNPCA); CO-OCCURRENCE MATRIX; STEGANALYSIS;

CRYPTOGRAPHY;

EID: 33845426373     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11922841_5     Document Type: Conference Paper

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