Al Zakitat, Mustafa Ali SahibAbdulrazzaq, Mohammed MajidRamaha, Nehad T. A.Mukhlif, Yasir AdilIsmael, Omar Ayad2024-05-312024-05-312024Al Zakitat, M. A. S., Abdulrazzaq, M. M., Ramaha, N. T. A., Mukhlif, Y. A., Ismael, O. A. (2024). Harnessing advanced techniques for image steganography: sequential and random encoding with deep learning detection. Lecture Notes in Networks and Systems, 960, 456-470. 10.1007/978-3-031-56728-5_3897830315672782367-3370https://hdl.handle.net/20.500.12939/4718Volume Editors : García Márquez F.P., Jamil A., Hameed A.A., Segovia Ramírez I.This study delves into the intricacies of steganography, a method employed for concealing information within a clandestine medium to enhance data security during transmission. Given that information is often represented in various forms, such as text, audio, video, or images, steganography offers a distinctive advantage over conventional cryptography by focusing on concealing the very existence of the message, rather than merely its content. This research introduces a novel steganographic technique that places equal emphasis on both message concealment and security enhancement. This study highlights two primary steganographic methods: sequential encoding and random encoding. By employing both encryption and image compression, these techniques fortify data security while preserving the visual integrity of cover images. Advanced deep learning models, namely Vgg-16 and Vgg-19, are proposed for the detection of image steganography, with their accuracy and loss rates rigorously evaluated. The significance of steganography extends across various sectors, including the military, government, and online domains, underscoring its pivotal role in contemporary data communication and security.eninfo:eu-repo/semantics/closedAccessArtificial neural networkCryptographData miningMachine learningNetwork securityConference Object9604564702-s2.0-85193627930Q4