Securing the Digital Data using SFLA based Deep Convolutional Neural Network in Medical Network Environment

As a result of the rapid development of multimedia in relation to network knowledge, accessing digital material has become relatively straightforward. Therefore, more emphasis on picture watermarking is required for IP protection. For this reason, many picture watermarking techniques were developed; nevertheless, they suffer from a lack of transparency and are not as sturdy. Multimedia copyright protection is very important in the field of digital watermarking. Digital picture watermarking is the process of secretly removing a watermark and inserting it into a carrier image. The success of digital watermarking in protecting digital data has attracted a lot of academic interest recently. There has been a lot of recent focus on using deep learning networks in tandem with wavelet-oriented strategies for picture watermarking. However, these benefits cannot be provided by traditional watermarking methods, which need manual methods for both extraction and embedding. In light of this problem, this research is driven to present a unique deep learning-based wavelet-based method for producing watermarked pictures with increased invisibility. A newly created Convolutional Neural Networks (CNN) model with medical pictures, and the deep features are retrieved utilising its fully connected layers. Next, the Shuffled frog-leaping algorithm (SFLA) is used to train the model, which ultimately results in higher quality classifications. The experimental results validate that the suggested model outperformed the baseline on a number of different measures, PSNR. In F $(228 \times 344)$ dataset thevWatermark1 ratio the speckle noise reach as 0.9950 and then gaussian noise reach as 0.9924 and then salt and peper noise reaches as 0.9972 respectively. And then the Watermark2 ratio the speckle noise reach as 0.9950 and then gaussian noise reach as 0.9924 and then salt and peper noise reaches as 0.9972 respectively.