An Image Hided-Data Detection Method Combining Markov Chain and Support Vector Machines

被引:0
|
作者
Zhao Huimin [1 ]
Zhu Li [2 ]
机构
[1] GuangDong Polytech Normal Univ, Coll Elect & Informat, Guangzhou 510665, Guangdong, Peoples R China
[2] GuangDong Polytech Normal Univ, Ind Training Ctr, Guangzhou 510665, Guangdong, Peoples R China
关键词
Data Detection; Markov Chain; Support Vector Machines; Prediction-error;
D O I
10.4028/www.scientific.net/AMM.128-129.520
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
An image hided-data detection method is proposed combining 2-D Markov chain model and Support Vector Machines (SVM) by the paper, in which image pixels are predicted with their neighboring pixels, and the prediction-error image is generated by subtracting the prediction value from the pixel value. Support vector machines are utilized as classifier. As embedding data rate being 0.1 bpp, experimental investigation utilizing spread spectrum (SS) and a Quantization Index Modulation (QIM) method data hiding method respectively, correction detection rates are all above 90%. For optimum LSB method,the method achieves a detection rate from 50% to 90% above with 0.01bpp-0.3bpp various embedding data rates.
引用
收藏
页码:520 / +
页数:2
相关论文
共 50 条
  • [1] Combining Support Vector Machines and Markov Random Fields in an Integrated Framework for Contextual Image Classification
    Moser, Gabriele
    Serpico, Sebastiano B.
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2013, 51 (05): : 2734 - 2752
  • [2] Combining support vector machines for accurate face detection
    Buciu, I
    Kotropoulos, C
    Pitas, I
    2001 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL I, PROCEEDINGS, 2001, : 1054 - 1057
  • [3] An incremental method combining density clustering and support vector machines for voice pathology detection
    Amami, Rimah
    Smiti, Abir
    COMPUTERS & ELECTRICAL ENGINEERING, 2017, 57 : 257 - 265
  • [4] COMBINING ENSEMBLE TECHNIQUE OF SUPPORT VECTOR MACHINES WITH THE OPTIMAL KERNEL METHOD FOR HYPERSPECTRAL IMAGE CLASSIFICATION
    Kuo, Bor-Chen
    Chen, I-Ling
    Li, Cheng-Hsuan
    Hung, Chih-Cheng
    2011 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2011, : 3903 - 3906
  • [5] A Method of Pulmonary Nodules Detection with Support Vector Machines
    Liu Lu
    Liu Wanyu
    ISDA 2008: EIGHTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 1, PROCEEDINGS, 2008, : 32 - 35
  • [6] Image tamper detection and classification using support vector machines
    Knowles, HD
    Winne, DA
    Canagarajah, CN
    Bull, DR
    IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, 2004, 151 (04): : 322 - 328
  • [7] Training Support Vector Machines on Large Sets of Image Data
    Kukenys, Ignas
    McCane, Brendan
    Neumegen, Tim
    COMPUTER VISION - ACCV 2009, PT III, 2010, 5996 : 331 - 340
  • [8] Prediction of displacement time series based on support vector machines-Markov chain
    Xu Fei
    Xu Wei-ya
    ROCK AND SOIL MECHANICS, 2010, 31 (03) : 944 - 948
  • [9] Support Vector Machines(SVM)-Markov Chain Prediction Model of Mining Water Inflow
    Kai HUANG
    Agricultural Science & Technology, 2017, 18 (08) : 1551 - 1554
  • [10] Prediction of displacement time series based on support vector machines-Markov chain
    Xu, Fei
    Xu, Wei-Ya
    Yantu Lixue/Rock and Soil Mechanics, 2010, 31 (03): : 944 - 948