HiFiMSFA: Robust and High-Fidelity Image Watermarking Using Attention Augmented Deep Network

Abstract

In recent years, the popularity of digital media sharing, especially high-quality images through online social networks (OSNs) has spurred an increasing demand for digital rights management (DRM) with watermarking. Although the most recent watermarking schemes with deep networks have exhibited considerable performance improvement, they still fall short in resisting multiple attacks with high-fidelity watermarking. To tackle this issue, a customized framework with encoder/decoder structure is proposed in this letter, aiming to consistently improve the robustness performance against multiple attacks. In specific, the Multi-scale Salient Feature Attention Block (MSFABlock) is exploited to effectively extract the robust image features with the encoder and decoder by taking advantage of the salient features, e.g., the image features obtained with difference of Gaussian (DoG) and other gradient operators. In addition, an adaptive squared Hinge function is developed as message loss to encourage adaptive watermark embedding. Experimental results demonstrate excellent performance in terms of robustness and perceptual fidelity as well as high efficiency of the proposed scheme in comparison to other SOTA methods.