A robust and efficient image watermarking scheme based on Lagrangian SVR and lifting wavelet transform

In this paper, an efficient image watermarking scheme based on Lagrangian support vector regression (LSVR) and lifting wavelet transform (LWT) is proposed to balance the trade-off between imperceptibility and robustness. LWT is faster and efficient implementation of traditional wavelet transform and LSVR has faster learning speed and high generalization capability compared to classical support vector regression. Combination of LWT and LSVR based image watermarking not only show imperceptibility and robustness but also reduces the time complexity. First the image is decomposed into four subbands using one level LWT and low frequency subband (approximate image) is partitioned into non overlapping blocks. Selected blocks based on correlation of wavelet coefficients are used to embed the binary watermark logo. For each selected block, significant wavelet coefficient is used as target to LSVR and its neighboring coefficients (feature vector) act as input to LSVR. The security of the watermark is achieved by applying Arnold transformation to original watermark. The scrambled watermark bit is embedded by comparing the output (predicted value) of LSVR of each selected block and the actual target value. The good learning capability and high generalization property of LSVR against noisy datasets provides the successful extraction of watermark against several image processing attacks: median filtering, average filtering, addition of Gaussian noise, salt and pepper noise, contrast enhancement, blurring, scaling, cropping and rotation. Experimental results show that the proposed scheme gives significant improvement in imperceptibility and robustness compared to the state-of-art techniques.