In this paper, a new robust watermarking technique for copyright protection based on Discrete Wavelet Transform and Singular Value Decomposition is proposed. The high frequency subband of the wavelet decomposed cover image is modified by modifying its singular values. A secret key is generated from the original watermark with the help of visual cryptography to claim the ownership of the image. The ownership of the image can be claimed by superimposing this secret key on the extracted watermark from the watermarked image. The robustness of the technique istested by applying different attacks and the visual quality of the extracted watermark after applying these attacks is good. Also, the visual quality of the watermarked image is undistinguishable from the original image.

No abstract

No abstract

Potential for exactly tracing maliciously altered pixels is currently desired in image authentication. This paper proposes an efficient pixel-wise fragile watermarking scheme, based on ARA (Authentication Relational & Associative) bits. In this scheme ARA bits are embedded as a watermark into the first three LSBs of each pixel of the host image. On the receiver end, by comparing the extracted LSBs and recalculated ARA bits, one can easily recognize the altered pixels of the host image.

In this paper, we propose a technique to generate DCT based unique normalized face using Principal Component Analysis (PCA). The idea of the PCA is to decompose face images into a small set of characteristic feature images. In the proposed technique we generate feature image by finding the peak values in the absolute DCT matrix followed by normalization. This maximizes the scatter between training dataset to give more discriminating power. The feature images so generated are called unique normalized faces as each image is different and unique from all other training faces. They have high recognition performance since they capture the global features onto a low dimensional linear "face space" extracted from the individual face of training dataset. We use Mahalanobis distance to measure the recognition between original face and the test face. The algorithm is tested on ORL face datasets. In the proposed technique we improved face recognition rate as compared to Eigenface, DCT-normalization and Wavelet-Denoising.