Lagrangian support vector regression based image watermarking in wavelet domain

To enhance the imperceptibility and robustness against image processing operations, the advantage of artificial neural network (ANN) and machine learning algorithms such as support vector regression (SVR), extreme learning machine (ELM) etc. are employed into watermarking applications. In this paper, Lagrangian support vector regression (LSVR) based blind image watermarking scheme in wavelet domain is proposed. The good learning capability, high generalization property against noisy datasets and less computational cost of LSVR compared to traditional SVR and ANN based algorithms makes the proposed scheme more imperceptible and robustness. Firstly, four sub images of host image are obtained using sub sampling. Each sub image is decomposed using discrete wavelet transform (DWT) to obtain the low frequency subband. Low frequency coefficients of each sub image are used to form the dataset act as input to LSVR. The output obtained by trained LSVR is used to embed the binary watermark. The security of the watermark is enhanced by applying Arnold transformation. Experimental results show the imperceptibility and robustness of the proposed scheme against several image processing attacks. The visual quality of watermarked image is quantified by the peak-signal-to noise ratio (PSNR) and the similarity between the original and extracted watermark is evaluated using bit error rate (BER). Performance of the proposed scheme is verified by comparing with the state-of-art techniques.