Gray scale image watermarking using fuzzy entropy and Lagrangian support vector regression in DCT domain

Abstract

A new image watermarking scheme based on fuzzy entropy and Lagrangian support vector regression (LSVR) in discrete cosine transform (DCT) domain is proposed in this paper. Fuzzy entropy is used to extract smooth non-overlapping blocks of an image followed by DCT to each selected block to transform it from spatial domain to frequency domain. Then a feature vector of low frequency DCT coefficients of each block act as an input to LSVR. The output (predicted value) of trained LSVR is used to embed the binary watermark by comparing it with the target value of the feature vector. As fuzzy entropy is able to discriminate data distribution under noise corrupted and redundant conditions, feature extraction is more robust against various attacks. The robustness of the proposed scheme is verified by performing various types of image processing operations which include geometric and non-geometric attacks. It is evident from experimental results that with the help of randomness measured of each image block using fuzzy entropy, good learning ability and high generalisation property of LSVR have made the proposed scheme more imperceptible and robust against different types of image processing operations.