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An Image Perceptual Hashing Algorithm Based on Convolutional Neural Networks

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Digital Forensics and Watermarking (IWDW 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14511))

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Abstract

The conventional perceptual hashing algorithms are constrained to a singular global feature extraction algorithm and lack efficient scalability adaptation. To address this problem, an image-perceptual hashing algorithm based on convolutional neural networks is proposed in this paper. First of all, the entire image is convolved by the backbone network to obtain a feature map. The Region Proposal Network (RPN) is employed to generate multiple-sized proposal frames at each location by using sliding windows. Considering the complexity and diversity of the object, proposal boxes of various sizes and shapes are formulated, and the local features are comprehensively exploited in an image, thereby, generating a perceptual hash code that can represent the semantic features of an image strongly. Moreover, The Mean Square Error (MSE) loss is incorporated into the optimization process to evaluate the coincidence between the proposal frame and the actual frame, generating more representative hash codes. Finally, an image perceptual hash code with high intuitive features can be formulated through iterative training of the proposed convolutional neural networks. Extensive experimental results demonstrate that the proposed image perceptual hashing algorithm based on a convolutional neural network surpasses other state-of-the-art methods.

This work was supported by National Natural Science Foundation of China (62272255, 62302248, 62302249); National key research and development program of China (2021YFC3340600, 2021YFC3340602); Taishan Scholar Program of Shandong (tsqn202306251); Shandong Provincial Natural Science Foundation (ZR2020MF054, ZR2023QF018, ZR2023QF032, ZR2022LZH011), Ability Improvement Project of Science and Technology SMES in Shandong Province (2022TSGC2485, 2023TSGC0217); Jinan “20 Universities”-Project of Jinan Research Leader Studio (2020GXRC056); Jinan “New 20 Universities”-Project of Introducing Innovation Team (202228016); Youth Innovation Team of Colleges and Universities in Shandong Province (2022KJ124); The “Chunhui Plan” Cooperative Scientific Research Project of Ministry of Education (HZKY20220482); Achievement transformation of science, education and production integration pilot project (2023CGZH-05), First Talent Research Project under Grant (2023RCKY131, 2023RCKY143), Integration Pilot Project of Science Education Industry under Grant (2023PX006, 2023PY060, 2023PX071).

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Authors and Affiliations

  1. Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

    Meihong Yang, Baolin Qi, Yongjin Xian & Jian Li

  2. Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China

    Meihong Yang, Baolin Qi, Yongjin Xian & Jian Li

Authors
  1. Meihong Yang
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  2. Baolin Qi
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  3. Yongjin Xian
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  4. Jian Li
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Corresponding author

Correspondence to Yongjin Xian .

Editor information

Editors and Affiliations

  1. School of Cyber Security, Qilu University of Technology, Jinan, China

    Bin Ma

  2. Qilu University of Technology, Jinan, China

    Jian Li

  3. Qilu University of Technology, Jinan, China

    Qi Li

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Yang, M., Qi, B., Xian, Y., Li, J. (2024). An Image Perceptual Hashing Algorithm Based on Convolutional Neural Networks. In: Ma, B., Li, J., Li, Q. (eds) Digital Forensics and Watermarking. IWDW 2023. Lecture Notes in Computer Science, vol 14511. Springer, Singapore. https://doi.org/10.1007/978-981-97-2585-4_7

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  • DOI: https://doi.org/10.1007/978-981-97-2585-4_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2584-7

  • Online ISBN: 978-981-97-2585-4

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