Robust Reversible Watermarking using Zero Mean Spreading Code

------------------------------------------------------------ABSTRACT--------------------------------------------------------Digital watermarking has been widely used to protect the copyright of digital images. Robust Reversible Watermarking (RRW) methods are popular in multimedia for the protection of data and providing robustness against unintentional attacks. This project presents a novel pragmatic framework, WaveletDomain Statistical Quantity Histogram Shifting and Clustering (WSQH-SC). WSQH-SC constructs a new watermark embedding and extraction procedures by histogram shifting and clustering, which are important for improving robustness and reducing run-time complexity. This proposes an image preprocessing method called Property Inspired Pixel Adjustment (PIPA). PIPA can effectively handle the overflow and underflow of pixels. For more security, zero mean spreading code is used before encryption. Furthermore, to increase the resolution with the reduction of data loss a single image super resolution method is used. This can be done by nearest embedding algorithm. So that, the secret input data is embedding only in the high resolution patches of the input image. K-means clustering algorithm is used to recover the watermarks by modeling the extraction process as a classification problem. Here the three parameters, such as Peak Signal to Noise Ratio(PSNR),Bit Error Rate(BER) and embedding capacity are estimated for the performance evaluation.