Perceptual Hashing based on Machine Learning for Blockchain and Digital Watermarking
Perceptual Hashing based on Machine Learning for Blockchain and Digital Watermarking
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DOI：
10.1109/worlds4.2019.8903993
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Meng Zhaoxiong;Morizumi Tetsuya;Miyata Sumiko;K. Hirotsugu
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作者：
Meng Zhaoxiong;Morizumi Tetsuya;Miyata Sumiko;K. Hirotsugu
在我们之前的研究中，我们发现了数字水印的三个要求。第一种是为了防止一幅图像的水印信息被转移到其他图像，该信息必须基于原始图像生成。第二个是，原始图像经过修改/编辑后，应该仍然能够与原始图像相同地使用。第三，多个数字水印应该在不依赖可信第三方的情况下存储和管理。为了满足这些需求，我们提出了一种基于感知哈希和区块链的数字版权管理系统。然而，因为我们在那项研究中使用了传统的感知散列，所以我们不能就第一和第二个要求得出足够的结论。在当前的研究中，为了获得稳定的消息摘要，我们提出了一种基于机器学习的改进感知散列方法。在该方法中，首先使用各种方法修改/编辑图像以生成图像集。然后将该图像集输入卷积神经网络(CNN)来计算图像的特征，并将CNN中间层的数据作为机器学习数据输出。最后，通过机器学习确定可用于计算潜在图像特征的潜在随机变量，并利用这些图像特征计算该图像集的感知哈希值，用于区块链和数字水印。该方法还将这些潜在的随机变量记录在区块链上，通过确保这些变量不被原始作者以外的人使用来确保版权安全。
In our previous study, we found three requirements for digital watermarking. The first is that to prevent watermark information of an image from being diverted to other images, this information must be generated based on the original image. The second is that after the original image is modified/edited, it should still be able to be used the same as the original image. The third is that multiple digital watermarks should be stored and managed without relying on trusted third parties. To meet these requirements, we proposed a digital-copyright-management system based on perceptual hashing and blockchain. However, because we used conventional perceptual hashing in that study, we could not draw sufficient conclusions about the first and second requirements. In this current study, to obtain a stable message digest, we propose a method of improving perceptual hashing based on machine learning. With this method, an image is first modified/edited using various methods to generate an image set. This image set is then input into a convolutional neural network (CNN) to calculate the features of the images, and the data of the CNN intermediate layer are output as machine learning data. Finally, through machine learning, latent stochastic variables are determined that can be used to calculate latent image features, and the perceptual hash value of this image set is calculated using these image features for the blockchain and digital watermarking. The method also records these latent stochastic variables on the blockchain to ensure copyright security by ensuring that these variables cannot be used by those other than the original author.