High-Precision Reversible Data Hiding Predictor: UCANet

Existing convolutional neural network-based reversible data hiding (RDH) predictors typically stack the standard convolution blocks with stride 1 for feature extraction, and keep the sizes of input and output feature maps unchanged through padding. This suggests that only a limited range of contextual spatial information is obtained. To remedy this problem above, a U-Net-like RDH predictor named UCANet is proposed in this paper to capture rich multi-scale contextual information by gradually downsampling feature maps. To fuse two feature maps at different levels along the channel dimension, we put forward the channel adaptive attention (CAA). By merely combining cheap pointwise convolution operations, CAA achieves the integration of non-linear and linear features as well as implicitly enhances channel dimensionality with low computational burden, thereby effectively enriching the expression of the channel information. The design of UCANet considers the characteristics of RDH from two aspects. On the one hand, instead of maxpooling or average pooling commonly used for downsampling, a stride-2 convolution block that can adaptively adjust the weights of convolution kernels and select useful information is utilized to downsample feature maps. On the other hand, UCANet removes the batch normalization layers to avoid their influence on the distribution of feature maps, which helps to strengthen the network's prediction capability. Extensive experiments also demonstrate that the proposed UCANet achieves better prediction performance, compared to several state-of-the-art methods.