A Normalized SVD based Image Watermarking with an Enhanced Robustness and Imperceptibility

Abstract: Digital Image watermarking is one of the most active and challenging subjects in the information hiding research because it is an efficient solution to protect the copyright of the digital media. This paper proposes a new robust watermarking approach based on the selection of singular features of host image into which the watermark has to embed. The host image initially subjected to a normalization process to find the invariant features and then the obtained invariant features are subjected to Integer Wavelet Transform (IWT) followed by Normalized Singular value Decomposition (NSVD). NSVD normalizes the singular values of invariant sub bands which can effectively resist the attacks. Genetic Algorithm (GA) is used to normalize the singular values. After, the obtained normalized singular values are modified by adding the singular values of watermark image. At the embedding phase, Particle Swarm Optimization (PSO) was used to optimize watermarking constant. Numerous experiments are conducted over the proposed approach to evaluate the performance. The obtained experimental results demonstrates that the proposed approach is superior compared to conventional approaches and is able to provide efficient resistance over Gaussian noise, salt & pepper noise, median filtering, cropping, rotation, contrast enhancement, scaling and Histogram Equalization attacks.