Double Branch Neural Network Watermarking Algorithm Based on Wavelet Decomposition and Dynamic Dense Dilated Convolution

doi:10.3969/j.issn.1671-1122.2025.05.014

Abstract

Abstract:

Deep learning based digital watermarking algorithms mainly tended to embed watermark information into the mid to high frequency regions of the carrier image. They only used convolutional neural networks to map the watermark information to the feature space of the carrier image and ignored the role of frequency domain algorithms. The article proposed a dual branch neural network watermarking algorithm based on wavelet decomposition and dynamic dense dilated convolution. By using wavelet decomposition, it better guided the embedding and extraction of watermark information. This algorithm used discrete wavelet transform to process carrier images and watermark images, decomposed them into high-frequency and low-frequency information, and then used neural networks for targeted learning. Dynamic dense dilated convolution was used to expand the receptive field, enhanced the ability to capture global information while reducing the number of neural network layers. It could also avoid using too many pooling layers that affected the quality of reconstructed images. The experiment show that the algorithm has good invisibility and robustness.

Key words: dual branch neural network, discrete wavelet transform, hollow convolution, digital watermark

CLC Number:

TP309

LI Jingyou, XI Xiaotian, WEI Rongle, ZHANG Guangda. Double Branch Neural Network Watermarking Algorithm Based on Wavelet Decomposition and Dynamic Dense Dilated Convolution[J]. Netinfo Security, 2025, 25(5): 828-839.

Figures/Tables 19

References 21

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噪声类型	编码图像${{I}_{en}}$	噪声图像${{I}_{no}}$	噪声图像和含水印图像差异放大20倍后的成像$20\left\| {{I}_{en}}-{{I}_{no}} \right\|$
像素替换
高斯滤波
近似JPEG
旋转
平移
椒盐噪声
中值滤波

原始载体图像${{I}_{co}}$	原始水印图像${{W}_{in}}$	提取出的水印图像${{W}_{out}}$	图像的残差成像\|${{W}_{in}}-~{{W}_{out}}$\|	图像的残差成像放大10倍后的成像10\|${{W}_{in}}-~{{W}_{out}}$\|

	PSNR	SSIM
平均值	39.49	0.9900
最大值	41.58	0.9908
最小值	37.79	0.9891

原始载体图像${{I}_{co}}$	融合图像${{I}_{en}}$	图像的残差成像\|${{I}_{co}}-~{{I}_{en}}$\|	残差放大10倍后的成像10\|${{I}_{co}}-~{{I}_{en}}$\|

	PSNR	SSIM
平均值	41.34	0.9907
最大值	43.22	0.9912
最小值	39.56	0.9900