Statistical Image Watermarking In DWT with Capacity Improvement

Abundant techniques has been widely used to design robust image watermarking schemes but in most cases due significance is not given on capacity and data imperceptibility aspects. Robustness of an image-watermarking scheme is the ability to detect the watermark after intentional attacks and normal audio/visual processes. This paper proposes a well-organized blind watermark detection scheme using DWT coefficients. Discrete Wavelet Transform (DWT) is widely applied to image watermarking applications because it decomposes a cover image into spatial domain as well as frequency domain simultaneously. The proposed method improves the capacity of image watermarking. The proposed paper concentrates on some of the main attributes necessary for image watermarking. They are embedding scheme, maximum likelihood detection, decision threshold, and the Laplacian model for image watermarking. The embedding method is multiplicative and done at second level of DWT decomposition by most favorable choice of the embedding strength. The watermark detection is based on the maximum likelihood ratio. Neyman-Pearson criterion is used to reduce the missed detection probability subject to a fixed false alarm probability. The DWT coefficients are assumed to be modeled using the Laplacian distribution. The proposed method is tested for imperceptibility, robustness, and capacity and proved to have better robustness and better imperceptibility and better capacity than other conventional watermarking techniques that were proposed earlier in literature

Blind colour image watermarking techniques in hybrid domain using least significant bit and slantlet transform

Colour image watermarking has attracted a lot of interests since the last decade in tandem with the rapid growth of internet and its applications. This is due to increased awareness especially amongst netizens to protect digital assets from fraudulent activities. Many research efforts focused on improving the imperceptibility or robustness of both semi-blind and non-blind watermarking in spatial or transform domain. The results so far have been encouraging. Nonetheless, the requirements of the watermarking applications are varied in terms of imperceptibility, robustness and capacity. Ironically, limited studies concern on the authenticity and blind watermarking. Hence, this study presents two new blind RGB image watermarking techniques called Model1 and Model2 in hybrid domain using Least Significant Bit (LSB) insertion and Slantlet Transform (SLT). The models share similar pre-processing and LSB insertion stages but differ in SLT approach. In addition, two interrelated watermarks known as main watermark (MW) and sub-watermark (SW) are also utilized. Firstly, the RGB cover image is converted into YCbCr colour space and then split up into three components namely, Y, Cb and Cr. Secondly, the Cb component is selected as a cover for the MW embedding using the LSB substitution to attain a Cb-watermarked image (CbW). Thirdly, the Cr component is chosen and converted into the transform domain using SLT, and is subsequently decomposed into two paths: three-level sub-bands for Model1 and two-level sub-bands for Model2. For each model, the sub-bands are then used as a cover for sub-watermark embedding to generate a Cr-watermarked image (CrW). Following that, the Y component, CbW and CrW are combined to obtain a YCbCr-watermarked image. Finally, the image is reverted to RGB colour space to attain the actual watermarked image (WI). Upon embedding, the MW and SW are extracted from WI. The extraction process is similar to the above embedding except it is accomplished in a reverse order. Experimental results which utilized the standard dataset with fifteen well-known attacks revealed that, among others: Model1 has produced high imperceptibility, moderate robustness and good capacity, with Peak Signal-to-Noise Ratio (PSNR) rose to 65dB, Normalized Cross Correlation (NCC) moderated at 0.80, and capacity was 15%. Meanwhile, Model2, as per designed, performed positively in all aspects, with NCC strengthened to 1.00, capacity jumped to 25% and PSNR softened at 55dB but still on the high side. Interestingly, in terms of authenticity, Model2 performed impressively albeit the extracted MW has been completely altered. Overall, the models have successfully fulfilled all the research objectives and also markedly outperformed benchmark watermarking techniques

Buyer-seller watermarking protocol in digital cinema

Master'sMASTER OF SCIENC

Probabilistic modeling of wavelet coefficients for processing of image and video signals

Statistical estimation and detection techniques are widely used in signal processing including wavelet-based image and video processing. The probability density function (PDF) of the wavelet coefficients of image and video signals plays a key role in the development of techniques for such a processing. Due to the fixed number of parameters, the conventional PDFs for the estimators and detectors usually ignore higher-order moments. Consequently, estimators and detectors designed using such PDFs do not provide a satisfactory performance. This thesis is concerned with first developing a probabilistic model that is capable of incorporating an appropriate number of parameters that depend on higher-order moments of the wavelet coefficients. This model is then used as the prior to propose certain estimation and detection techniques for denoising and watermarking of image and video signals. Towards developing the probabilistic model, the Gauss-Hermite series expansion is chosen, since the wavelet coefficients have non-compact support and their empirical density function shows a resemblance to the standard Gaussian function. A modification is introduced in the series expansion so that only a finite number of terms can be used for modeling the wavelet coefficients with rendering the resulting PDF to become negative. The parameters of the resulting PDF, called the modified Gauss-Hermite (NIGH) PDF, are evaluated in terms of the higher-order sample-moments. It is shown that the MGH PDF fits the empirical density function better than the existing PDFs that use a limited number of parameters do. The proposed MGH PDF is used as the prior of image and video signals in designing maximum a posteriori and minimum mean squared error-based estimators for denoising of image and video signals and log-likelihood ratio-based detector for watermarking of image signals. The performance of the estimation and detection techniques are then evaluated in terms of the commonly used metrics. It is shown through extensive experimentations that the estimation and detection techniques developed utilizing the proposed MGH PDF perform substantially better than those that utilize the conventional PDFs. These results confirm that the superior fit of the MGH PDF to the empirical density function resulting from the flexibility of the MGH PDF in choosing the number of parameters, which are functions of higher-order moments of data, leads to the better performance. Thus, the proposed MGH PDF should play a significant role in wavelet-based image and video signal processin