Enhanced CNN DCT Steganography: Deep Learning BasedImageSteganography

Abstract

Protecting data on the internet from attacks and unauthorized access requires informationsecurity. The complementary techniques of cryptography and steganography are highlightedfor ensuring the confidentiality and integrity of shared data. Cryptography converts messagesinto ciphertext to conceal their content, whereas steganography conceals the existenceof datawithin seemingly innocent carriers like text or graphics. Combining the two methods results inacomprehensive solution that protects communication from potential adversaries by addinglevels of obscurity and secrecy. By fusing DGP with an XOR-based embedding technique, thestudy suggests a solution that improves data security while providing remarkableimperceptibility.Secure communication has become increasingly important and challenginginthe digital era, which has resulting in the disclosure of sophisticated information-hidingstrategies.Using deep learnig technologies, this work offers an innovative solution for imagesteganography that enhances the security and usability of encoding confidential data intooverphotos.Conventional steganographic techniques frequently have capacity, resilience, andsmall limitations that leave them open to discovery and extraction by unauthorized parties. I suggesta deep learning-based image steganography method that uses convolutional neural networks(CNN) to discover the best embedding strategies for secret images in order to overcomethesedifficulties. Because the model was trained on a variety of cover and secret image datasets, itwas possible to integrate the secret data while preserving visual consistency. By offering a reliable method for securely transmitting data or information to others, this studyadvances the subject of data and information security. Other ambiguous phrases usedhereinclude digital watermarking and secure transmission. Additionally, copyright protectionhasbeen obtained by applicants.