Robust Image Steganography in Online Social Networks via Neural Style Transfer

Existing style-transfer steganography schemes suffer from three critical limitations: insufficient robustness against online social network (OSN) processing pipelines, susceptibility to steganalytic detection, and degraded visual quality. To address these challenges holistically, we propose StegTransfer—a unified framework that integrates: (1) forward non-differentiable distortion simulation, which emulates realistic OSN operations to enhance robustness; (2) adversarially hardened embedding through joint training with steganalyzers to improve security; and (3) payload-preserving style enhancement that optimizes visual aesthetics without sacrificing embedding capacity. Experimental evaluations demonstrate that StegTransfer achieves superior performance in visual fidelity (NIMA score: 6.32), robustness (PSNR up to 30.2 dB under JPEG compression), and security (detection rates as low as 15.5% and 62.3% under StegExpose and SiaStegNet, respectively.