Linear Discriminate Analysis based Robust Watermarking in DWT and LWT Domain with PCA based Statistical Feature Reduction

Abstract

With aiming to design a novel image watermarking technique, this paper presents a novel method of image watermarking using lifting wavelet transform, discrete wavelet transform, and one-dimensional linear discriminate analysis. In this blind watermarking technique, statistical features of the watermarked image have been incorporated for preparing the training set and testing set. After that, the principal component analysis is applied to reduce the obtained feature set, so the training time is reduced to the desired level and accuracy is enhanced. The one-dimensional linear discriminate analysis is used for binary classification as it has the ability to classify with good accuracy. This technique applies discrete wavelet transform and lifting wavelet transform in two different watermarking schemes for the image transformation. Both transformations give higher tolerance against image distortion than other conventional transformation methods. One of the significant challenges of a watermarking technique is maintaining the proper balance between robustness and imperceptibility. The proposed blind watermarking technique exhibits the imperceptibility of 43.70 dB for Lena image in case of no attack for the first scheme (using LWT) and 44.71 dB for the second scheme (using DWT+LWT). The first watermarking scheme is tested for robustness, and it is seen that the given scheme is performing well against most of the image attacks in terms of robustness. This technique is compared using some existing similar watermarking methods, and it is found to be robust against most image attacks. It also maintains the excellent quality of the watermarked image.