Conditional Flow-Based Generative Steganography

Generative steganography (GS) is a novel data-hiding technique that generates stego images directly from secret data without using cover images, which is different from traditional steganography. However, existing steganography methods have shortcomings in terms of hiding capacity, extraction accuracy, and diversity of stego images. To address these limitations, we propose a high-performance Conditional Flow-based Generative Steganography (CFGS). First, to achieve exact extraction of secret data in high-capacity scenarios, we hide secret data in the frequency domain to resist the impact of stego image distortion. In addition, to enhance the diversity of stego images, we introduce a novel conditional generative flow model (C-Flow) to generate stego images, which consists of two newly designed layers, the Conditional Attention-based Affine Coupling layer and the Conditional Invertible Norm layer. C-Flow can accurately guide the visual content of stego images through different conditions, enhancing the diversity of stego images. Our approach is the first GS method capable of conditional guidance of stego image visual content, and achieves extraction accuracy of hidden secret data equal to or close to 100% for payloads up to 1 bit-per-pixel (bpp). Extensive experiments demonstrate that our proposed approach outperforms state-of-the-art GS methods.