AFcIHNet: Attention feature-constrained network for single image information hiding

Single image information hiding aims to obtain a stegano image by hiding a secret image within a cover image. Recently, deep neural network (DNN) based information hiding methods have been advanced extensively. However, it is difficult for them to preserve the spatial information at different scales on each channel feature map in the stegano image, largely because of a lack of effective features and feature fusion. In this paper, a bi-channel attention feature-constrained pixel-shuffle information hiding network (AFcIHNet) is proposed to efficiently enhance the detailed spatial information in the stegano image. First, the invertible neural network (INN) is employed for the information hiding task, which is adaptively constrained by the proposed bi-channel attention module (Bi-AM). The capability of the Bi-AM module is further enhanced by the introduction of a novel attention loss function. In addition, to take full advantage of the attention based feature fusion mechanism that combines the global and local contexts of the features, we used the Squeeze and Concat (SPC) module with adaptive branching factors, the model can hierarchically approach the input feature map from local to global. Extensive experiments on the DIV2K and COCO datasets show that the proposed method can produce competitive stegano images when compared with some state-of-the-art methods. Finally, we can get a secret image that is closer to the original image. Peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and average pixel difference (APD) were used to evaluate the experimental effects of cover and stegano images as well as secret and recovered secret images. The experimental results show that the obtained stegano and cover images are more indistinguishable, and the recovered secret image is closer to the original image. Overall, the overall performance is improved by nearly 0.8% compared to the existing baseline model.