Abstract—In this paper, we have used Least-Squares Support
Vector Regression (LS-SVR) method, which is a reliable and
robust method for regression analysis, for grayscale image
watermarking in DCT domain. This method offers several
advantages unlike conventional SVRs which is perceived as a
minimization problem with linear inequality constraints and
has solution to quadratic programming (QP) problem. Due to
this reason, the problem solution becomes computationally
costly. On the other hand, the solution to the LS-SVR algorithm
may be obtained by solving a system of linear equations instead
of solving a QP problem and therefore it consumes less time. In
this case, the LS-SVR algorithm embeds a given binary
watermark in three different grayscale images in a short time
span. PSNR values indicate good quality of the signed images.
The watermarks are also extracted and the computed values of
SIM(X,X *) correlation parameter indicate that the
extraction process is quite successful.
Index Terms—Grayscale image watermarking; least-squares support vector regression; PSNR.
Vikash Chaudhary is with Department of Computer Science, BN College, University of Delhi, Delhi, India (email@example.com).
Anurag Mishra is with Department of Physics and Electronics, DDUC, University of Delhi, Delhi, India.
Rajesh Mehta is with Department of Computer Science, Amity Engineering College, GGSIP University, Delhi, India.
Monika Verma is with MJKP School, Delhi, India.
Ram Pal Singh is with the Department of Computer Science, DDUC, University of Delhi, Delhi, India ( firstname.lastname@example.org)
Navin Rajpal is with University School of Information Technology, GGSIP University, Delhi, India.
Cite:Vikash Chaudhary, Anurag Mishra, Rajesh Mehta, Monika Verma, Ram Pal Singh, and Navin Rajpal, "Watermarking of Grayscale Images in DCT Domain Using Least-Squares Support Vector Regression," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 725-728, 2012.