Generative Steganographic Flow
Ping Wei Ge Luo Qi Song Xinpeng Zhang Zhenxing Qian Sheng Li
Generative steganography (GS) is a new data hiding manner, featuring direct
generation of stego media from secret data. Existing GS methods are generally
criticized for their poor performances. In this paper, we propose a novel flow
based GS approach -- Generative Steganographic Flow (GSF), which provides
direct generation of stego images without cover image. We take the stego image
generation and secret data recovery process as an invertible transformation,
and build a reversible bijective mapping between input secret data and
generated stego images. In the forward mapping, secret data is hidden in the
input latent of Glow model to generate stego images. By reversing the mapping,
hidden data can be extracted exactly from generated stego images. Furthermore,
we propose a novel latent optimization strategy to improve the fidelity of
stego images. Experimental results show our proposed GSF has far better
performances than SOTA works.