01989nas a2200253   4500000000100000000000100001008004100002260001200043653003200055653002200087653003400109653002400143653002900167653001700196100002000213700002000233700001800253245008100271856008200352300001200434490000600446520126900452022001401721       2021                            d  c12/202110aDiscrete Wavelet Transforms10aRecurrent Network10aLong Short Term Memory (LSTM)10aSimulated Annealing10aTunicate Swarm Algorithm10aWatermarking1 aR. Radha Kumari1 aV. Vijaya Kumar1 aK. Rama Naidu00aOptimized DWT Based Digital Image Watermarking and Extraction Using RNN-LSTM  uhttps://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_14_0.pdf  a150-1620 v73 aThe 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.  a1989-1660