Contourlet-based image and video watermarking robust to geometric attacks and compressions

In this paper, we first propose a new blind image watermarking scheme robust to geometric attacks and compressions. The scheme is based on contourlet transform (CT) and principal component analysis (PCA). The scheme uses the principal components of the largest contourlet coefficients of the last directional subband of the cover image to embed the watermark. Meanwhile, with the noise visibility function (NVF), the watermarking strength is adjusted adaptively to preserve the perceptual quality of the image. The watermark can be detected with high accuracy after various possible distortions. The normalized correlation (NC) between the original watermark and the watermark extracted from the distorted watermarked image is used as the robustness evaluation criterion. The simulation results demonstrate that the proposed scheme has good performance in terms of both quality and robustness against a variety of image-processing attacks, such as rotation, scaling and image compressions. Then we extend the scheme to blind video watermarking. The performance of the video watermarking scheme is evaluated against video attacks like rotation, frame averaging, noise additions and video compressions. The introduction of the CT produces robustness against image and video compressions, and the PCA yields resistance to geometric attacks.