Deep learning steganography for big data security using squeeze and excitation with inception architectures.
Authors
Affiliations (5)
Affiliations (5)
- Dept. of Computer Science & Engineering, Amal Jyothi College of Engineering, APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, 695 016, India.
- Dept. of Electrical and Electronics Engineering, Amal Jyothi College of Engineering, Kanjirappally, 686518, Kerala, India. [email protected].
- Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India. [email protected].
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint AbdulRahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia. [email protected].
- EIAS Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia.
Abstract
With the exponential growth of big data in domains such as telemedicine and digital forensics, the secure transmission of sensitive medical information has become a critical concern. Conventional steganographic methods often fail to maintain diagnostic integrity or exhibit robustness against noise and transformations. In this study, we propose a novel deep learning-based steganographic framework that combines Squeeze-and-Excitation (SE) blocks, Inception modules, and residual connections to address these challenges. The encoder integrates dilated convolutions and SE attention to embed secret medical images within natural cover images, while the decoder employs residual and multi-scale Inception-based feature extraction for accurate reconstruction. Designed for deployment on NVIDIA Jetson TX2, the model ensures real-time, low-power operation suitable for edge healthcare applications. Experimental evaluation on MRI and OCT datasets demonstrates the model's efficacy, achieving Peak Signal-to-Noise Ratio (PSNR) values of 39.02 and 38.75, and Structural Similarity Index (SSIM) values of 0.9757, confirming minimal visual distortion. This research contributes to advancing secure, high-capacity steganographic systems for practical use in privacy-sensitive environments.