摘要： 图像隐写是指将秘密图像隐藏到载体图像中，生成含密图像并在公共信道中传输，通常包括秘密图像的嵌入和提取两个过程。现有多载体图像隐写方法将秘密图像的嵌入过程拆分为编码和叠加两步，将秘密图像编码为含密扰动，通过空域操作将含密扰动与多张载体图像叠加，实现了在多张载体图像中嵌入秘密图像。这种方法的嵌入和提取两个互逆过程分别由两个相互独立的网络实现，无法共享参数，这导致计算资源消耗大、训练参数多。为解决这个问题，提出了一种基于可逆神经网络的多载体图像隐写模型，它将嵌入和提取过程分别与可逆神经网络的正向和逆向映射相关联，实现了参数共享，有效减少了网络参数量。此外，现有的模型缺乏对秘密图像重要内容级区域的重要性度量方法。针对此问题，在可逆神经网络输入端引入了空域注意力模块，以提高编码质量，关注秘密图像中的关键区域，从而提升隐写效果。同时，为多用户配给基于密钥的身份信息矩阵，建立了身份核验机制，防止攻击者非法获取秘密图像。实验表明，该方法实现了较好的隐写效果，含密图像和提取出的秘密图像的峰值信噪比优于基线模型8.5dB~9.4dB，结构相似度优于基线模型0.012~0.019、学习感知图像块相似度优于基线模型0.0029~0.0047，参数量仅为基线模型的17.6%

Abstract: Image steganography refers to the technique of hiding a secret image within a cover image, creating a container image, and transmitting it over a public channel. Existing multi-cover image steganography methods involves two processes: embedding and extraction of the secret image. Existing multi-cover image steganography methods often decompose the embedding process into encoding and overlaying steps. The secret image is encoded into a secret disturbance and the secret disturbance is overlaid with multiple cover images using spatial operations, implementing enable the embedding of a secret image within multiple cover images. These methods employ two separate networks for the embedding and extraction processes, which are not parameter-sharing, resulting in high computational resource consumption and a large number of training parameters. To solve this problem, a multi-cover image steganography model based on invertible neural network is proposed. It associates the embedding and extraction processes with the forward and inverse mappings of a invertible neural network, enabling parameter sharing and effectively reducing the network parameter count. Furthermore, existing models lack a method for measuring the importance of content-level regions in the secret image. To tackle this problem, a spatial attention module is introduced at the input of the invertible neural network to enhance the encoding quality, focusing on key regions of the secret image and improving the steganography performance. Additionally, an identity verification mechanism is established by allocating a key-based identity information matrix to multiple users, preventing unauthorized access to the secret image. The experimental results demonstrate that the proposed method achieves superior steganography performance compared to baseline models. The peak signal-to-noise ratio of the container image and extracted secret images surpass the baseline model by 8.5 dB to 9.4 dB, respectively. The structural similarity index outperforms the baseline model with a margin of 0.012 to 0.019, and the learned perceptual image patch similarity outperforms the baseline model with a margin of 0.0029~0.0047. Moreover, the proposed model requires only 17.6% of the parameters compared to the baseline model.