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DSTNet: Distinguishing Source and Target Areas for Image Copy-Move Forgery Detection

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Pattern Recognition (ICPR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15322))

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Abstract

In copy-move forgery detection, most relevant studies concern locating the copy-move areas without the distinction of source and target regions. This paper proposes an end-to-end network, DSTNet, to identify the source and target based on consistency detection between the copy-move region and the non-copy-move region. The DSTNet is composed of two stages, the Pre-processing stage and the Discrimination stage. Pre-processing Stage extracts internal information of copy-move and non-copy-move areas and conducts a series of operations to meet the requirements of network input. Discrimination stage allows multiple patches for input and classifies the input patches. Specifically, the Pre-processing stage, contains the Copy-move Patches Selection (CM Patches Selection) and Genuine Patches Selection, can select pairs of copy-move and none copy-move patches. We train the proposed DSTNet on two large synthetic datasets and use the public datasets CASIA and Comofod for evaluation. The experiment shows that our method achieves excellent results. Particularly, we achieve a 5.4% higher F1 based on ground-truth of copy-move mask (GT-CM) on CASIA dataset.

This work was supported by the Science and Technology Development Fund of Macau SAR (Grant number 0045/2022/A), and the Macao Polytechnic University (Project No. RP/FCA-12/2022).

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Acknowledgements

This work was supported by the Science and Technology Development Fund of Macau SAR (Grant number 0045/2022/A), and the Macao Polytechnic University (Project No. RP/FCA-12/2022).

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Authors and Affiliations

  1. School of Cyberspace Security, Shandong University of Political Science and Law, Jinan, China

    Kaiqi Zhao

  2. Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR, China

    Xiaochen Yuan

  3. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

    Guoheng Huang

  4. School of Information Science and Engineering, University of Jinan, Jinan, China

    Kun Liu

Authors
  1. Kaiqi Zhao
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  2. Xiaochen Yuan
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  3. Guoheng Huang
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  4. Kun Liu
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Corresponding author

Correspondence to Xiaochen Yuan .

Editor information

Editors and Affiliations

  1. University of Salford, Salford, Lancashire, UK

    Apostolos Antonacopoulos

  2. Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Subhasis Chaudhuri

  3. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

  4. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  5. IIT Kharagpur, Kharagpur, West Bengal, India

    Saumik Bhattacharya

  6. Indian Statistical Institute Kolkata, Kolkata, West Bengal, India

    Umapada Pal

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Zhao, K., Yuan, X., Huang, G., Liu, K. (2025). DSTNet: Distinguishing Source and Target Areas for Image Copy-Move Forgery Detection. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15322. Springer, Cham. https://doi.org/10.1007/978-3-031-78312-8_21

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  • DOI: https://doi.org/10.1007/978-3-031-78312-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-78311-1

  • Online ISBN: 978-3-031-78312-8

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