A Deep Learning-Based Hybrid Method for Reliable and Imperceptible Data Hiding

Problem: In the deep image steganography field, the main challenge is to achieve a balance between visual image quality, reliably recovering the message, robustness, and interpretability, especially regarding image distortion because of noise, attack, resizing, and cropping. Solution: In this paper, we propose to combine deterministic pattern-based embedding with a deep neural refinement network to achieve a strong balance between robustness, simplicity, and quality. Methodology: First of all, we embed binary messages using spatial patterns, then refine the stego image, using an encoder–decoder network and enhanced with an attention mechanism. Results: The experimental results record PSNR values between 34.9 and 37.8 dB and SSIM values above 0.99, with zero BER under no-attack, noise, and resizing conditions. Moderate degradation is observed under blur (BER ≈ 0.125), while cropping significantly affects performance (BER ≈ 0.575). Contribution: The proposed approach introduces an interpretable and stable hybrid design that effectively balances imperceptibility and robustness, while maintaining reliable message recovery in practical scenarios. The use of differentiable attacks through training enhances robustness against common distortions such as noise, blur, and resizing.