Hasan Abdulrahman, Northern Technical University, Iraq

Social-media steganography is a hostile-channel problem because platforms such as Facebook can resize images, recompress them as JPEG, modify chroma infor-mation, remove metadata, quantize colors, and apply proprietary optimizations that destroy fragile hidden payloads. This paper presents FBStegNet, an end-to-end deep neural framework for robust binary data hiding in color images transmitted through Facebook-like processing. The key contributions are fourfold: a dense message encoder that distributes payload redundancy across the image, a residual CNN feature extrac-tor that identifies stable cover structures, a differentiable Facebook simulation layer that models randomized JPEG compression, downsampling–upsampling, color quanti-zation, and signal-dependent noise, and a texture-aware attention embedding network that controls where and how strongly information is embedded. By training the en-coder and decoder over a stochastic distribution of social-media distortions, FBSteg-Net optimizes message recovery after platform processing rather than only preserving clean-image fidelity. In a representative 0.05 bpp case study, FBStegNet reduces the bit-error rate (BER) to 0.018 in simulated Facebook-like tests, compared with 0.482 for LSB matching, 0.214 for DCT robust embedding, and 0.167 for generic neural hiding, while preserving 38.94 dB PSNR and 0.958 SSIM. In real Facebook upload–download experiments, it achieves 0.027 BER with 38.51 dB PSNR and 0.951 SSIM, demonstrating that the learned embedding remains recoverable under practical social-media transformations. Additionally, this work specifies a reproducible evaluation pro-tocol using steganography image benchmarks, Facebook-style simulated channels, real upload–download tests, payload metrics, perceptual-quality measures, and steganaly-sis checks. FBStegNet therefore provides a practical and extensible design for robust secret message communication over lossy social-media image pipelines.

Information Hiding, Social Media, Deep Learning, Image Steganography