- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Enhanced image steganalysis through reinforcement learning, generative adversarial networks, and vision transformers (RGV-Stega) | ||
| Journal of University of Anbar for Pure Science | ||
| Volume 20, Issue 1, April 2026, Pages 312-321 PDF (1.59 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.37652/juaps.2025.165053.1707 | ||
| Authors | ||
| Sumia Abdulhussien Razooqi Al-obaidi1; Mohammed Ahmed Talab2; Mohanaad Shakir3; Mustafa Talal Alnaseri4; Suryanti Awang* 5 | ||
| 1Ministry of Higher Education and Scientific Research Baghdad, Iraq | ||
| 2Department of Medical Physics College of Applied Science Al-Fallujah University Anbar, Iraq | ||
| 3College of Business(COB) University of Buraimi, Oman | ||
| 4Faculty of Electrical and Computer Engineering University of Tabriz, Tabriz, Iran | ||
| 5Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah 26600, Pekan, Pahang, Malaysia | ||
| Abstract | ||
| As steganographic techniques advance and object data are increasingly generated in various domains, detecting concealed data embedded in digital media has become more challenging; thus, image steganalysis plays an important role. Conventional detection techniques are becoming less effective against these evolving strategies, which calls for better solutions. In this paper, we present RGV-Stega, a new high-level structure that combines Reinforcement Learning (RL), Generative Adversarial Networks (GANs), and Vision Transformers (ViTs) to further enhance the detection of steganographic contents in images. Artificial intelligence techniques such as RL, GANs, and ViTs play significant roles in current steganalysis work: RL helps an agent learn optimal feature extraction strategies, GANs yield rich steganographic samples for strong training, and ViTs help capture long-range dependencies in image data, thereby boosting top-1 accuracy. On benchmark datasets such as BOSSBase and BOWS, our method RGV-Stega achieves up to 93% accuracy, comparable to conventional CNN- and ViT-based models. Findings show that combining RL, GANs, and ViTs is an effective approach to addressing issues arising from advances in steganographic techniques. This improves steganalysis’s ability to identify hidden content. | ||
| Keywords | ||
| Generative adversarial network; Image steganalysis; Reinforcement learning; Transfer learning; Vision transformers | ||
|
Statistics Article View: 399 PDF Download: 410 |
||