Image watermarking in LWT domain based on nonnegative matrix factorization and singular value decomposition
Pranab Kumar Dhar, Tetsuya Shimamura · 2014
We present a blind watermarking method in lifting wavelet transform (LWT) domain based on nonnegative matrix factorization (NMF) and singular value decomposition (SVD) for image copyright protection. The watermark image is preprocessed first using a Gaussian map in order to enhance the confidentiality. LWT is then applied to the original image to get sub-bands and the low frequency sub-band is divided into blocks. NMF is performed on each of these blocks to get the nonnegative matrix and weight matrix. Weight matix of each block is decomposed into three matrices using SVD. The largest singular value of each weight matrix is selected for embedding watermark using a quantization function. Simulation results indicate that the proposed method shows high robustness against different attacks. Moreover, it outperforms state-of-the-art methods in terms of invisibility and robustness.