Efficient Cross-Scale Invertible Hiding Network with Spatial-Frequency Collaboration and Non-Invertible Mechanism
This work addresses the limitations of single-scale and single-domain feature extraction in INN-based image hiding, offering a more effective solution for concealing and revealing images.
The paper proposes CrosInv, an invertible neural network for image hiding that uses cross-scale and spatial-frequency collaboration with a non-invertible mechanism to improve image quality, achieving superior performance over existing methods.
Image hiding aims to conceal image-level messages within cover images at the same resolution. Invertible neural networks (INN)-based image hiding has emerged as an important branch. It treats concealing and revealing as a pair of inverse problems on image domain transformation and uses INN's forward and backward processes to address them. Due to architectural constraints, existing INN-based methods suffer from single-scale and single-domain feature extraction and limited nonlinear representation capability, resulting in inferior image quality. To mitigate these limitations, we propose an efficient cross-scale invertible hiding network with the spatial-frequency collaboration and the non-invertible mechanism, termed CrosInv. CrosInv exploits cross-scale and spatial-frequency collaborative features while enhancing nonlinear representation. Specifically, we introduce a cross-scale invertible module that bijectively maps inputs to cross-scale representations. To effectively integrate spatial and frequency information, the cross-scale invertible module employs pixel shuffle, Haar wavelet transformation, and their inverse operations for scale transformation. Furthermore, a non-invertible cross dense module is integrated to enhance the nonlinearity. Comprehensive experiments verify the effectiveness and superiority of the proposed CrosInv.