skip to main content
10.1145/3589335.3651540acmconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
short-paper

Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining

Published: 13 May 2024 Publication History

Abstract

In copy-move tampering operations, perpetrators often employ techniques, such as blurring, to conceal tampering traces, posing significant challenges to the detection of object-level targets with intact structures. Focus on these challenges, this paper proposes an Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining (IMNet). To obtain complete object-level targets, we customize prototypes for both the source and tampered regions and dynamically update them. Additionally, we extract inconsistent regions between coarse similar regions obtained through self-correlation calculations and regions composed of prototypes. The detected inconsistent regions are used as supplements to coarse similar regions to refine pixel-level detection. We operate experiments on three public datasets which validate the effectiveness and the robustness of the proposed IMNet.

Supplemental Material

MP4 File
Supplemental video

References

[1]
Mauro Barni, Quoc Tin Phan, and Benedetta Tondi. 2020. Copy Move Source- Target Disambiguation through Multi-Branch CNNs. IEEE Transactions on Information Forensics and Security PP, 99 (2020), 1--1.
[2]
Beijing Chen, Weijin Tan, Gouenou Coatrieux, Yuhui Zheng, and Yun-Qing Shi. 2020. A serial image copy-move forgery localization scheme with source/target distinguishment. IEEE Transactions on Multimedia 23 (2020), 3506--3517.
[3]
Davide Cozzolino, Giovanni Poggi, and Luisa Verdoliva. 2015. Efficient densefield copy--move forgery detection. IEEE Transactions on Information Forensics and Security 10, 11 (2015), 2284--2297.
[4]
Jing Dong, Wei Wang, and Tieniu Tan. 2013. Casia image tampering detection evaluation database. In 2013 IEEE China Summit and International Conference on Signal and Information Processing. IEEE, 422--426.
[5]
Ashraful Islam, Chengjiang Long, Arslan Basharat, and Anthony Hoogs. 2020. DOA-GAN: Dual-order attentive generative adversarial network for image copymove forgery detection and localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4676--4685.
[6]
Yaqi Liu, Chao Xia, Xiaobin Zhu, and Shengwei Xu. 2021. Two-Stage Copy-Move Forgery Detection With Self Deep Matching and Proposal SuperGlue. IEEE Transactions on Image Processing 31 (2021), 541--555.
[7]
Chi-Man Pun, Xiao-Chen Yuan, and Xiu-Li Bi. 2015. Image forgery detection using adaptive oversegmentation and feature point matching. ieee transactions on information forensics and security 10, 8 (2015), 1705--1716.
[8]
Dijana Tralic, Ivan Zupancic, Sonja Grgic, and Mislav Grgic. 2013. CoMo- FoD-New database for copy-move forgery detection. In Proceedings ELMAR-2013. IEEE, 49--54.
[9]
Mayank Verma and Durgesh Singh. 2023. Survey on image copy-move forgery detection. Multimedia Tools and Applications (2023), 1--37.
[10]
Jingyu Wang, Xuesong Gao, Jie Nie, Xiaodong Wang, Lei Huang, Weizhi Nie, Mingxing Jiang, and Zhiqiang Wei. 2024. Strong robust copy-move forgery detection network based on layer-by-layer decoupling refinement. Information Processing & Management 61, 3 (2024), 103685. https://doi.org/10.1016/j.ipm. 2024.103685
[11]
Yue Wu, Wael Abd-Almageed, and Prem Natarajan. 2018. Busternet: Detecting copy-move image forgery with source/target localization. In Proceedings of the European conference on computer vision (ECCV). 168--184.
[12]
Yue Wu, Wael Abd-Almageed, and Prem Natarajan. 2018. Image copy-move forgery detection via an end-to-end deep neural network. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 1907--1915.
[13]
Yulan Zhang, Guopu Zhu, Xing Wang, Xiangyang Luo, Yicong Zhou, Hongli Zhang, and Ligang Wu. 2023. CNN-Transformer Based Generative Adversarial Network for Copy-Move Source/ Target Distinguishment. IEEE Trans. Circuits Syst. Video Technol. 33, 5 (2023), 2019--2032. https://doi.org/10.1109/TCSVT.2022. 3220630
[14]
Kaiqi Zhao, Xiaochen Yuan, Tong Liu, Yan Xiang, Zhiyao Xie, Guoheng Huang, and Li Feng. 2024. CAMU-Net: Copy-move forgery detection utilizing coordinate attention and multi-scale feature fusion-based up-sampling. Expert Systems with Applications 238 (2024), 121918.
[15]
Jun-Liu Zhong and Chi-Man Pun. 2019. An end-to-end dense-inceptionnet for image copy-move forgery detection. IEEE Transactions on Information Forensics and Security 15 (2019), 2134--2146.
[16]
Ye Zhu, Chaofan Chen, Gang Yan, Yingchun Guo, and Yongfeng Dong. 2020. ARNet: Adaptive attention and residual refinement network for copy-move forgery detection. IEEE Transactions on Industrial Informatics 16, 10 (2020), 6714--6723.

Cited By

View all
  • (2024)Copy-move detection method based on Decoupled Edge Supervision and multi-domain cross correlation modelingMultimedia Tools and Applications10.1007/s11042-024-19584-zOnline publication date: 19-Jul-2024

Index Terms

  1. Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    WWW '24: Companion Proceedings of the ACM Web Conference 2024
    May 2024
    1928 pages
    ISBN:9798400701726
    DOI:10.1145/3589335
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 13 May 2024

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. copy-move
    2. deep learning
    3. forgery image detection
    4. inconsistency mining

    Qualifiers

    • Short-paper

    Funding Sources

    Conference

    WWW '24
    Sponsor:
    WWW '24: The ACM Web Conference 2024
    May 13 - 17, 2024
    Singapore, Singapore

    Acceptance Rates

    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)83
    • Downloads (Last 6 weeks)6
    Reflects downloads up to 07 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Copy-move detection method based on Decoupled Edge Supervision and multi-domain cross correlation modelingMultimedia Tools and Applications10.1007/s11042-024-19584-zOnline publication date: 19-Jul-2024

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media