论文标题
盲目水印：结合可逆和不可逆转的机制
Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms
论文作者
论文摘要
盲水印为版权保护，图像身份验证和篡改识别提供了有力的证据。但是，对于强烈的噪音攻击，设计具有高度不可识别和鲁棒性的水印模型仍然是一个挑战。为了解决这个问题，我们提出了一个结合了可逆和不可变化（CIN）机制的框架。 CIN由可逆零件组成，以实现高易用性和不可变形的部分，以增强对强噪声攻击的鲁棒性。对于可逆部件，我们开发一个扩散和提取模块（DEM）以及一个融合和拆分模块（FSM）以可逆的方式对称地嵌入和提取水印。对于不可逆转的部分，我们引入了一个不可固化的基于注意力的模块（NIAM）和特定于噪声的选择模块（NSM），以解决强烈的噪声攻击下的不对称提取。广泛的实验表明，我们的框架的表现优于当前的最不可能和鲁棒性的方法。我们的框架在无噪声条件下平均可以达到99.99％的精度和67.66 dB PSNR，而96.64％和39.28 dB结合了强烈的噪声攻击。该代码将在https://github.com/rmpku/cin中提供。
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.
