Perceptual Hashing based on Machine Learning for Blockchain and Digital Watermarking

Zhaoxiong Meng, Tetsuya Morizumi, Sumiko Miyata, Hirotsugu Kinoshita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019
EditorsXin-She Yang, Nilanjan Dey, Amit Joshi
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages193-198
Number of pages6
ISBN (Electronic)9781728137803
DOIs
Publication statusPublished - 2019 Jul
Event3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019 - London, United Kingdom
Duration: 2019 Jul 302019 Jul 31

Publication series

NameProceedings of the 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019

Conference

Conference3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019
CountryUnited Kingdom
CityLondon
Period19/7/3019/7/31

Keywords

  • Blockchain
  • Digital copyright management
  • Digital watermarking
  • Machine learning
  • perceptual hashing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management
  • Renewable Energy, Sustainability and the Environment
  • Safety, Risk, Reliability and Quality
  • Education

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  • Cite this

    Meng, Z., Morizumi, T., Miyata, S., & Kinoshita, H. (2019). Perceptual Hashing based on Machine Learning for Blockchain and Digital Watermarking. In X-S. Yang, N. Dey, & A. Joshi (Eds.), Proceedings of the 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019 (pp. 193-198). [8903993] (Proceedings of the 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WorldS4.2019.8903993