NDLI: An Image Steganographic Scheme Based on Support Vector Regression

Content Provider	IEEE Xplore Digital Library
Author	Hsien-Chu Wu Kuo-Ching Liu Jun-Dong Chang Ching-Hui Huang
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Nat. Chung Hsing Univ., Taichung (Jun-Dong Chang; Ching-Hui Huang) \|\| Grad. Sch. of Comput. Sci. & Inf. Technol., Nat. Taichung Inst. of Technol., Taichung (Hsien-Chu Wu) \|\| Dept. of Med. Lab. Sci. & Biotechnol., China Med. Univ., Taichung (Kuo-Ching Liu)
Abstract	This paper presents a novel image steganographic method that utilizes support vector regression (SVR) to predict the embedded pixel value such that secret data is also embedded into the pixel-value difference between the predicted pixel value and the original pixel value. Due to the significant learning ability in the correlations of training samples by support vector regression, the trained SVR function is obtained by neighboring pixels of the sample pixels to predict the embedded pixel values, and then the proposed scheme uses pixel-value differences to embed the secret data. In the data extraction phase, the proposed scheme uses trained SVR function to predict the embedded pixel value, and the secret data is extracted from pixel-value differences. Experimental results show that SVR is good at learning the correlations of neighboring pixel, and the proposed scheme also has reliable security, high embedding capacity and better image quality for the stego-image.
Starting Page	519
Ending Page	524
File Size	1005376
Page Count	6
File Format	PDF
ISBN	9780769533827
DOI	10.1109/ISDA.2008.145
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-26
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	data hiding Data mining machine learning pixel-value difference Smart pixels Image quality Computer science support vector regression Steganography Cities and towns Data encapsulation Internet Cryptography Biomedical imaging
Content Type	Text
Resource Type	Article

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