الفهرس 
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Abstract
As the Internet becomes more and more populous, people concern more about the copyright protection issue for digital data such as images, video and audio. Digital watermarking is a process of embedding data, called watermark, into a multimedia object such that it can be extracted later to make an assertion about the object. Therefore, it can protect the copyright. The applications include authentication, fingerprinting, broadcast monitoring and copy protection. We propose an image watermarking for protecting the copyright.
This thesis discusses the important properties of watermarking techniques, review many approaches and provides a new robust blind image-watermarking algorithm. The proposed algorithm is based on both the Discrete Wavelet Transform (DWT) and the Wavelet Packet Transform (WPT). The main idea of the proposed algorithm is to decompose the host image using DWT and WPT according to the size of the watermark. The watermark is embedded in the fine-scale bands of the WPT of the fine-scale bands of the last DWT decomposition level of the host image. This method of embedding improves the robustness and imperceptibility of the watermark. Each pixel in the watermark is split into three parts, and then the One-Bit per Coefficient (OBC) approach is applied for embedding the pixel. The final step is to compute the Inverse Wavelet Packet Transform (IWPT) and the Inverse Discrete Wavelet Transform (IDWT) to obtain the watermarked image. In this algorithm, the watermark is extracted from the watermarked image with no need of the original host image. The obtained watermarked image has very high Peak Signal to Noise Ratio (PSNR). Using the proposed embedding method, the PSNR of watermarked image is about 52.16 dB for watermark of size (64x64) embedded in an original cover image (Lena image) of size (512x512). The extracted watermark has a Normalized Correlation (NC) of one. The proposed’ gray-scale image watermarking technique is straightforwardly extended to color images by applying it to each color component individually. The proposed technique has been compared with other published techniques by measuring their robustness
against different attacks. The proposed algorithm is robust to a variety of signal operations, such as compression (e.g. JPEG comp Gaussian noise, filtering, sharpening, blurring, and image