aspg-3-3380-1791417155 20261007235235 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 17 2 Mostafa A. Ahmad Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia Eftkhar Al-Qhtani Department of Information Systems, College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia Ahmed H. Samak Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia Amr Ibrahim Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia Mourad Elloumi Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia Ali Ahmed Information technology Department, Faculty of Computers and Information, Menoufia University, Egypt Steganalysis can be defined as the science that addresses the process of identifying and detecting hidden information or data within various types of digital media. Recently, Deep Learning (DL) approaches have been employed to build steganalysis systems. However, the problem with steganalysis systems adopting a DL approach is their low accuracy and their need for effective datasets to be used for the training. In this paper, we introduce a DL-based Steganalysis system for the detection and classification of hidden content in images. Our system, called Steg-Analysis Convolutional Neural Network (SA-CNN), relies on a Convolutional Neural Network (CNN) and uses High Pass Filter (HPF) and extra-embedded data. We also propose a preprocessing-based data hiding method to increase the accuracy of SA-CNN in detecting hidden content. Therefore, this ensures the imperceptibility of images used for training SA-CNN. In addition, we use another CNN, called Malicious-Benign Classification CNN (MBC-CNN), that we have developed to classify the extracted hidden content into Malicious or Benign classes. Compared with existing systems, SA-CNN shows a better performance in terms of accuracy, under increased hiding rates ranging from 0.1 to 1.0 bpp, reaching 90%. 2025 377 393 3380 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.170228 https://www.americaspg.com/journal/3/article/3380 https://www.americaspg.com/storage/81733994340.pdf