HFRW: High Fidelity and Robust Watermarking Using Deep Reinforcement Learning

Deep learning-based watermarking technology has made significant success due to its excellent robustness and convenient traceability. However, previous studies rarely considered users’ demands for high-fidelity images and file size growth rates, especially in scenarios involving the watermarking of a large number of high-resolution images. This paper proposes a high fidelity and robust watermarking using deep reinforcement learning. Specifically, we propose a self-optimization module that utilizes a dueling Q-learning network to select the optimal watermark embedding patch, which minimizes the quality impact after adding a watermark to the image. Additionally, we incorporate a convolutional block attention module (CBAM) into encoder and decoder networks to enhance feature learning in both frequency and spatial dimensions. Experimental results demonstrate that our method significantly enhances image fidelity, achieving PSNR values above 54dB across different datasets. The method also exhibits good robustness against common image manipulations, including both non-geometric and geometric attacks. Furthermore, our method achieves an FSVR value below 0.5, reducing file size inflation by a factor of 34 compared to existing advanced methods. This makes it very friendly for storing a large number of high-resolution watermarked images.