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Blind watermarking provides powerful evidence for copyright protection, imageauthentication, and tampering identification. However, it remains a challengeto design a watermarking model with high imperceptibility and robustnessagainst strong noise attacks. To resolve this issue, we present a frameworkCombining the Invertible and Non-invertible (CIN) mechanisms. The CIN iscomposed of the invertible part to achieve high imperceptibility and thenon-invertible part to strengthen the robustness against strong noise attacks.For the invertible part, we develop a diffusion and extraction module (DEM) anda fusion and split module (FSM) to embed and extract watermarks symmetricallyin an invertible way. For the non-invertible part, we introduce anon-invertible attention-based module (NIAM) and the noise-specific selectionmodule (NSM) to solve the asymmetric extraction under a strong noise attack.Extensive experiments demonstrate that our framework outperforms the currentstate-of-the-art methods of imperceptibility and robustness significantly. Ourframework can achieve an average of 99.99% accuracy and 67.66 dB PSNR undernoise-free conditions, while 96.64% and 39.28 dB combined strong noise attacks.The code will be available in https://github.com/rmpku/CIN.

Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms