Large Scale Dual Tree Complex Wavelet Transform based robust features in PCA and SVD subspace for digital image watermarking
Jyotsna Yadav, Khushwant Sehra · Procedia Computer Science · 2018
A new watermarking scheme based on robust feature extraction using dual tree complex wavelet transform (DTCWT) in low dimensional subspace for grayscale imagesis proposed in this work. First the decomposition of host image is performed using DTCWT where large scale features having low frequency coefficients are extracted. The features are then further analyzed using the score matrix obtained from principal component analysis (PCA). Singular values from the score matrix are then obtained using the singular value decomposition (SVD) in a lower dimensional sub space. The watermark is subjected to similar processing after being scrambled using Arnold transform. The resultant robust lower dimensional DTCWT – PCA – SVD features are combined with the features extracted from the watermark. The robustness of the technique is evaluated by calculating quality assessment parameters. Further the robustness is evaluated according to the StirMark benchmarks by subjecting the signed image to various image processing attacks. High PSNR and correlation values as compared to state of art techniques indicate the imperceptibility and robustness of the proposed scheme.