Robust Generative Image Steganography based on Frequency Domain using Fourier Transform

Generative steganography is a steganography method that uses a generator to convert secret messages into realistic images. It has received widespread attention due to its ability to resist steganalysis. However, existing methods suffer from poor quality of generated stego images and the inability to withstand losses during complex social media transmission processes. In response to these issues, this article proposes a new frequency-domain diffusion generative steganography method that can achieve secure and robust steganography without the need for training or fine-tuning the network. In addition, we also studied the inherent errors in the bidirectional mapping of diffusion models and proposed solutions. The experimental results demonstrate the excellent performance of our method in terms of extraction accuracy, robustness, security, and image quality.