Deep steganographic approach for reliable data hiding using convolutional neural networks and adaptive loss optimization

This paper proposes a deep steganography framework using a three-layered Convolutional Neural Network (CNN) architecture-preparation, hiding, and revealing networks-for robust data hiding. The preparation network employs 50, 10, and 5 filters (3×3, 4×4, 5×5) for edge feature extraction, followed by adaptive embedding in the hiding network. Optimized with four loss functions, Loss Function 3 (LF 3), integrating mean and variance terms, achieves a payload of 3-5 bits per pixel and improved PSNR across Tiny-ImageNet, Linnaeus 5 dataset, Sky-text dataset and RGB-BMP datasets. LF 3 ensures robustness against Gaussian noise, cropping, and rotation, with low detection rates in histogram, statistical, and CNN-based steganalysis. Compared to state-of-the-art Generative Adversarial Network (GAN) based methods, LF 3 offers higher payload-robustness balance and computational efficiency, advancing secure data hiding for applications like medical imaging.