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Image Copy Detection Based on Convolutional Neural Networks

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Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 663))

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Abstract

In this paper, we present a model that automatically differentiates copied versions of original images. Unlike traditional image copy detection schemes, our system is a Convolutional Neural Networks (CNN) based model which means that it does not need any manually-designed features. In addition, a convolutional network is more applicable to image copy detection whose architecture is designed for robustness to geometric distortions. Our model uses fully connected layers to compute a similarity between CNN features, which are extracted from image pairs by a deep convolutional network. This method is very efficient and scalable to large databases. In order to see the comparison visually, a variety of models are explored. Experimental results demonstrate that our model presents surprising performance on various data sets.

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Acknowledgement

This work is partly supported by the 973 basic research program of China (Grant No. 2014CB349303), the Natural Science Foundation of China (Grant No. 61472421), the National Nature Science Foundation of China (No. 61370038) and the Strategic Priority Research Program of the CAS (Grant No. XDB02070003).

Author information

Authors and Affiliations

  1. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, 300300, China

    Jing Zhang & Jinfeng Yang

  2. National Laboratory of Pattern Recognition, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, No. 95, Zhongguancun East Road, Beijing, 100190, China

    Wenting Zhu, Bing Li & Weiming Hu

Authors
  1. Jing Zhang
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  2. Wenting Zhu
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  3. Bing Li
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  4. Weiming Hu
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  5. Jinfeng Yang
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Corresponding author

Correspondence to Bing Li .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Zhang, J., Zhu, W., Li, B., Hu, W., Yang, J. (2016). Image Copy Detection Based on Convolutional Neural Networks. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_10

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  • DOI: https://doi.org/10.1007/978-981-10-3005-5_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3004-8

  • Online ISBN: 978-981-10-3005-5

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