Traditional image watermarking algorithms directly modify the host image by watermark embedding, which is hard to balance the contradiction between the robustness and imperceptibility. Inspired by the human brain’s associative memory, this paper proposes a non-embedding and blind image watermarking algorithm via mapping based Residual Convolution Neural Network (Mapping-based RCNN). For preprocessing, median filter is applied on the host image to enhance the robustness of the algorithm to against various attacks. After that, Discrete Cosine Transform and Singular Value Decomposition are adopted to extract the corresponding image information matrix. To obtain the mapping relationship between host image and watermark image, the information matrix is input into the designed Mapping-based RCNN structure for network training. The Mapping-based RCNN is a non-embedding watermarking algorithm, which not only overcomes the imperceptibility shortcoming but also wins good robustness compared with traditional watermarking algorithms. Experimental results show that the proposed algorithm can successfully extract the watermark images under various attacks, and is more robust than existing watermarking algorithms.

传统的图像水印算法通过水印嵌入直接修改宿主图像，很难在鲁棒性和不可感知性之间取得平衡。受人脑联想记忆的启发，本文提出了一种基于映射残差卷积神经网络（基于映射的RCNN）的非嵌入和盲图像水印算法。对于预处理，将中值滤波器应用于宿主图像，以增强算法抵抗各种攻击的鲁棒性。之后，采用离散余弦变换和奇异值分解提取对应的图像信息矩阵。为了获得主机图像和水印图像之间的映射关系，将信息矩阵输入到设计的基于映射的RCNN结构中以进行网络训练。基于映射的RCNN是一种非嵌入水印算法，与传统的水印算法相比，它不仅克服了不可感知性的缺点，而且具有很好的鲁棒性。实验结果表明，该算法能够在各种攻击下成功提取水印图像，并且比现有的水印算法具有更强的鲁棒性。