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Steganalysis Using Histogram Characteristic and Statistical Moments of Wavelet Subbands  

Hyun, Seung-Hwa (School of Electronics Eng., Pusan National University)
Park, Tae-Hee (Dept. Mechatronics Eng., TongMyong University)
Kim, Young-In (Dept. Biomedical Eng., Pusan National University)
Kim, Yoo-Shin (School of Electronics Eng., Pusan National University)
Eom, Il-Kyu (School of Electronics Eng., Pusan National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.47, no.6, 2010 , pp. 57-65 More about this Journal
Abstract
In this paper, we present a universal steganalysis scheme. The proposed method extract features of two types. First feature set is extracted from histogram characteristic of the wavelet subbands. Second feature set is determined by statistical moments of wavelet characteristic functions. 3-level wavelet decomposition is performed for stego image and cover image using the Haar wavelet basis. We extract one features from 9 high frequency subbands of 12 subbands. The number of second features is 39. We use total 48 features for steganalysis. Multi layer perceptron(MLP) is applied as classifier to distinguish between cover images and stego images. To evaluate the proposed steganalysis method, we use the CorelDraw image database. We test the performance of our proposed steganalysis method over LSB method, spread spectrum data hiding method, blind spread spectrum data hiding method and F5 data hiding method. The proposed method outperforms the previous methods in sensitivity, specificity, error rate and area under ROC curve, etc.
Keywords
steganalysis; wavelet subbend; probability density function; characteristic function; statistical moment;
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