A new zero-watermarking algorithm based on deep learning

Jing Liu, Qian Li, Hui Yang
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Abstract

A new zero-watermarking algorithm based on deep learning is proposed to improve the robustness of the zero-watermarking, in which zero-watermarking image generation and copyright verification are both completed using neural networks. First, a stylized image is generated from a host image and a logo image with a time stamp through VGG network. Then, the stylized image is encrypted by the Arnold transform and registered as a zero-watermarking image in Intellectual Property Protection (IPR). Finally, the RCNN network is designed to extract the logo image to verify the copyright of host images. The experimental results show that the security and robustness of the algorithm are better than the existing zero-watermarking algorithm.
一种新的基于深度学习的零水印算法
为了提高零水印的鲁棒性,提出了一种基于深度学习的零水印算法,该算法利用神经网络完成零水印图像的生成和版权验证。首先,通过VGG网络从主机图像和带有时间戳的徽标图像生成风格化图像。然后,对程式化后的图像进行阿诺德变换加密,并在知识产权保护(IPR)中注册为零水印图像。最后,设计RCNN网络提取标识图像,验证主机图像的版权。实验结果表明,该算法的安全性和鲁棒性都优于现有的零水印算法。
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