Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms
This work addresses the problem of copyright protection and image authentication for digital media creators, representing an incremental improvement over existing methods.
The paper tackles the challenge of designing a blind watermarking model with high imperceptibility and robustness against strong noise attacks by presenting a framework combining invertible and non-invertible mechanisms, achieving an average of 99.99% accuracy and 67.66 dB PSNR under noise-free conditions and 96.64% accuracy and 39.28 dB PSNR under combined strong noise attacks.
Blind watermarking provides powerful evidence for copyright protection, image authentication, and tampering identification. However, it remains a challenge to design a watermarking model with high imperceptibility and robustness against strong noise attacks. To resolve this issue, we present a framework Combining the Invertible and Non-invertible (CIN) mechanisms. The CIN is composed of the invertible part to achieve high imperceptibility and the non-invertible part to strengthen the robustness against strong noise attacks. For the invertible part, we develop a diffusion and extraction module (DEM) and a fusion and split module (FSM) to embed and extract watermarks symmetrically in an invertible way. For the non-invertible part, we introduce a non-invertible attention-based module (NIAM) and the noise-specific selection module (NSM) to solve the asymmetric extraction under a strong noise attack. Extensive experiments demonstrate that our framework outperforms the current state-of-the-art methods of imperceptibility and robustness significantly. Our framework can achieve an average of 99.99% accuracy and 67.66 dB PSNR under noise-free conditions, while 96.64% and 39.28 dB combined strong noise attacks. The code will be available in https://github.com/rmpku/CIN.