Analysis of Digital Watermarking Techniques Using Transform-Based Function

Security and protection of digital data is always a challenging job over the internet. For the protection and copyright of digital multimedia data used digital watermarking techniques. The digital watermarking techniques protect the copyright protection of digital multimedia data. Digital watermarking techniques used to transform based function for the processing of watermark embedding. The transform-based function is a texture feature dominated property. The texture feature is the most important part of the digital image. In this paper study and analysis of transform-based digital image watermarking. The transform-based digital image watermarking using the function of DCT, DWT, IWT and SHIFT. In the family of transform, the function used the layered transform function is wavelet transform function, and other is the shift key point transform function. For simulation used MATLAB software and used standard image dataset and symbol for the process of embedding. For the validation of transform function estimate, four well know parameter such as encoding time, decoding time, PSNR, and the value of NC

Optimized DWT Based Digital Image Watermarking and Extraction Using RNN-LSTM

The rapid growth of Internet and the fast emergence of multi-media applications over the past decades have led to new problems such as illegal copying, digital plagiarism, distribution and use of copyrighted digital data. Watermarking digital data for copyright protection is a current need of the community. For embedding watermarks, robust algorithms in die media will resolve copyright infringements. Therefore, to enhance the robustness, optimization techniques and deep neural network concepts are utilized. In this paper, the optimized Discrete Wavelet Transform (DWT) is utilized for embedding the watermark. The optimization algorithm is a combination of Simulated Annealing (SA) and Tunicate Swarm Algorithm (TSA). After performing the embedding process, the extraction is processed by deep neural network concept of Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM). From the extraction process, the original image is obtained by this RNN-LSTM method. The experimental set up is carried out in the MATLAB platform. The performance metrics of PSNR, NC and SSIM are determined and compared with existing optimization and machine learning approaches. The results are achieved under various attacks to show the robustness of the proposed work