Typical steganography embeds secret information into images by exploiting their redundancy. Since the visual imperceptibility of secret information is a key factor in scheme evaluation, conventional methods aim to balance this requirement with embedding capacity. Consequently, integrating emerging image generation models and secret transmission has been extensively explored to achieve a higher embedding capacity. Previous works mostly focus on generating stego-images with Generative Adversarial Networks (GANs) and usually rely on pseudo-keys, namely conditions or parameters involved in the generation process, which are related to secret images. However, studies on diffusion-based coverless steganography remain insufficient. In this work, we leverage the Denoising Diffusion Implicit Model (DDIM) to generate high-quality stego-images without introducing pseudo-keys, instead employing real keys to enhance security. Furthermore, our method offers low-image-correlation real-key protection by incorporating chaotic encryption. Another core innovation is that our method requires only one-time negotiation for multiple communications, unlike prior methods that necessitate negotiation for each interaction.

翻译：典型的隐写术通过利用图像的冗余性将秘密信息嵌入其中。由于秘密信息的视觉不可感知性是方案评估的关键因素，传统方法旨在平衡这一要求与嵌入容量。因此，将新兴图像生成模型与秘密传输相结合以实现更高嵌入容量已得到广泛探索。先前的研究大多集中于使用生成对抗网络（GANs）生成隐写图像，并通常依赖于伪密钥，即生成过程中涉及的条件或参数，这些条件或参数与秘密图像相关。然而，基于扩散模型的无载体隐写研究仍不充分。在本工作中，我们利用去噪扩散隐式模型（DDIM）生成高质量隐写图像，无需引入伪密钥，而是采用真实密钥以增强安全性。此外，通过结合混沌加密，我们的方法提供了低图像相关性的真实密钥保护。另一核心创新在于，我们的方法仅需一次协商即可支持多次通信，而不同于先前方法需要在每次交互时进行协商。