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使用频域卷积的端到端图像数字盲水印方法

End-to-end Blind Image Watermarking on Frequency Domain

  • 摘要: 传统数字水印方法对裁剪、噪声、形变等攻击具有强鲁棒性,  但很难抵御真实场景中由图像压缩编码和移动摄像设备翻拍引起的水印信息丢失. 为了增强水印鲁棒性,  利用离散余弦变换频谱中某些频段对人眼的掩蔽特性以及卷积神经网络对一些不可见扰动的学习能力,  提出一种基于频域卷积的端到端图像数字盲水印方法. 首先,  使用卷积神经网络构建编码网络,  将水印信息嵌入到图像频域中; 其次,构建与编码网络对称的解码网络,  从图像频域中提取水印信息; 最后,  对编码和解码网络进行联合训练,并监督编码网络的图像质量和解码网络的水印提取效果. 在MIRFLICKR数据集上进行的实验结果表明,  该方法的PSNR, SSIM, LPIPS, BPP,  显示器下翻拍的准确率分别达到了36.29dB, 0.951, 3.11×10-3, 2.44×10-3, 93.1%,  与其他基准方法相比具有一定的优势,  证明了该方法的有效性.

     

    Abstract: Traditional digital watermarking methods are robust to attacks such as crop, noise, and deformation, but it is difficult to resist the loss of watermarking caused by image compression coding and mobile camera flipping in real world. To enhance the robustness, an end-to-end digital blind watermarking method based on frequency domain convolution is proposed by exploiting the masking property of some frequency bands in the discrete cosine transform spectrum to human eyes and the learning ability of convolutional neural network to some invisible perturbations. First, the convolutional neural network is used to build an encoding network to embed the watermarking information into the image frequency domain; second, a decoding network symmetric to the encoding network is built to extract the watermarking information from the image frequency domain; finally, the encoding and decoding networks are jointly trained and the image quality and the watermarking extraction effect are supervised. The experimental results on the MIRFLICKR show that PSNR, SSIM, LPIPS, BPP and the accuracy under-display flipping of the method reach 36.29dB, 0.951, 3.11×10-3, 2.44×10-3 and 93.1% respectively, which have advantages over other benchmark methods and prove the effectiveness of the method.

     

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