Neural Network-Driven Image Hiding Using Multi-Scale Feature Fusion

Image-hiding technology plays a vital role in ensuring data security and privacy across various sensitive fields. However, traditional methods often struggle to strike an optimal balance between embedding capacity and image quality, which can lead to performance degradation. Additionally, these conventional approaches tend to lack robustness in complex scenarios, limiting their practical application. To address these challenges, the study introduces a novel image-hiding technique driven by neural networks. By leveraging multi-channel convolutional neural networks and multi-scale feature fusion, the method incorporates a dual-channel feature processing and fusion module, along with a multi-stage embedding and restoration module. Experimental results demonstrate that the proposed approach consistently surpasses existing techniques in terms of embedding capacity, visual quality, and restoration accuracy, particularly in relative capacity measures, indicating greater embedding efficiency. Ablation studies further confirm the significant contribution of each module to the overall performance of the model.