Malik Kaleem Razzaq, Sajid Muhammad, Almogren Ahmad, Malik Tauqeer Safdar, Khan Ali Haider, Altameem Ayman, Rehman Ateeq Ur, Hussen Seada
Department of Computer Science, Air University, Islamabad, 44230, Pakistan.
Chair of Cyber Security, Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11633, Saudi Arabia.
Sci Rep. 2025 Jun 4;15(1):19630. doi: 10.1038/s41598-025-01054-7.
The growth of the internet and big data has spurred the demand for more extensive information hoarding to store and distribute information. In today's digital era, ensuring the security of data transmission is paramount. Advancements in digital technology have facilitated the proliferation of high-resolution graphics over the Internet, raising security concerns and enabling unauthorized access to sensitive data. Researchers have increasingly explored steganography as a reliable method for secure communication because it plays a crucial role in concealing and safeguarding sensitive information. This study introduces a novel and comprehensive steganography framework using the discrete cosine transform (DCT) and the deep learning algorithm, generative adversarial network. By leveraging deep learning techniques in both spatial and frequency domains, the proposed hybrid architecture offers a robust solution for applications requiring high levels of data integrity and security. While conventional steganography methods are typically classified into spatial and transform domains, extensive research and analysis demonstrate that the hybrid approach surpasses individual techniques in performance. The experimental results validate the effectiveness of the proposed steganography approach, showcasing superior visual image quality with a mean square error (MSE) of 93.30%, peak signal-to-noise ratio (PSNR) of 58.27%, root mean squared error (RMSE) of 96.10%, and structural similarity index measure (SSIM) of 94.20%, in comparison to existing leading methodologies. The proposed model achieved reconstruction accuracies of 96.2% using Xu Net and 95.7% with SR Net. By combining DCT with deep learning algorithms, the proposed approach overcomes the limitations of spatial domain methods, offering a more flexible and effective steganography solution. Furthermore, simulation results confirm that the proposed technique outperforms state-of-the-art methods across key performance metrics, including MSE, PSNR, SSIM, and RMSE.
互联网和大数据的发展刺激了对更广泛信息存储以存储和分发信息的需求。在当今数字时代，确保数据传输安全至关重要。数字技术的进步促进了高分辨率图形在互联网上的扩散，引发了安全担忧，并使得敏感数据能够被未经授权访问。研究人员越来越多地探索将隐写术作为一种可靠的安全通信方法，因为它在隐藏和保护敏感信息方面起着至关重要的作用。本研究引入了一种新颖且全面的隐写术框架，该框架使用离散余弦变换（DCT）和深度学习算法——生成对抗网络。通过在空间和频域中利用深度学习技术，所提出的混合架构为需要高水平数据完整性和安全性的应用提供了一个强大的解决方案。虽然传统的隐写术方法通常分为空间域和变换域，但广泛的研究和分析表明，混合方法在性能上超越了单独的技术。实验结果验证了所提出的隐写术方法的有效性，与现有的领先方法相比，其展示出了卓越的视觉图像质量，平均平方误差（MSE）为93.30%，峰值信噪比（PSNR）为58.27%，均方根误差（RMSE）为96.10%，结构相似性指数测量（SSIM）为94.20%。所提出的模型使用徐网（Xu Net）实现了96.2%的重建准确率，使用SR网（SR Net）实现了95.7%的重建准确率。通过将DCT与深度学习算法相结合，所提出的方法克服了空间域方法的局限性，提供了一种更灵活、有效的隐写术解决方案。此外，仿真结果证实，所提出的技术在包括MSE、PSNR、SSIM和RMSE在内的关键性能指标上优于现有技术方法。
