GURNet: A Gated U-Shaped Encoder-Decoder Predictor for Reversible Data Hiding
Shaowei Weng, Haiyang Rao, Lifang Yu, Liwen Chen · IEEE Signal Processing Letters · 2025
The existing deep learning based reversible data hiding (RDH) predictors typically adopt standard convolutions for extracting features, which inherently fails to capture contextual information across different scales, making the model have difficulty to fully understand the image content. To this end, a gated multi-scale module (GMM) is proposed to enrich and strengthen feature representations by collecting multi-scale features with less computational cost using a set of parallel depthwise convolutions, and customizing the gated convolution (GConv) for RDH to weight the importance of features in channel and spatial dimensions. Considering that directly utilizing the addition or concatenation operations cannot better fuse two types of features with different receptive fields, a gated feature fusion and refinement module (GFFRM) is tailored to employ the standard convolutions of different sizes to shorten the receptive field differences between deep and shallow features. GFFRM also constructs depthwise separable convolution followed by GConv to enrich and refine the expression of features at low computational cost and enhance the information exchange across channel and spatial dimensions, thereby improving the fusion effect of features at different levels. A two-path multi-dimensional feature interaction module (MFIM) is designed, where one branch utilizes a pointwise convolution to obtain low-dimensional representations of features, whereas the other branch fuse two linearly transformed features through element- wise multiplication constructs to generate implicit high-dimensional features. GFFRM and MFIM are complementary for each other to enhance the prediction performance. Three modules, namely GMM, GFFRM and MFIM, are embedded in U -shaped encoder-decoder architecture to establish a novel RDH predictor GURNet. Extensive experiments implemented on four publicly available datasets demonstrate the superiority of GURNet, compared with state-of-the-art RDH predictors.