Skip to main content
SpringerLink
Log in

An automatic enhanced filters with frequency-based copy-move forgery detection for social media images

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Using images in various file types has become common in the modern digital world, such as social media posts, research reports, and legal documents. The availability of low-cost image manipulation tools has made it easier to change images, potentially leading to undetected image fraud. One such form of image manipulation is copy-move forgery (CMF), which is difficult to detect due to the similarities in image features. There have been efforts to detect CMF using copy-move forgery detection (CMFD) methods. However, most research has focused on CMF images with attacks rather than social media. Social media has contributed significantly to the image manipulation phenomenon, and additional post-processing techniques on social media platforms have affected the efficiency of the CMFD methods. Therefore, this research proposes a two-stage pre-processing phase combined with frequency-based CMFD to detect CMF images in different social media platforms. The first stage includes automatic image selection, followed by image enhancement with filters to improve image quality. The experimental results show that the proposed method achieves the highest detection score compared to existing CMFD methods, with an average score of 90% for CMF-Facebook, 91% for CMF-WhatsApp, and 85% for CMF-Twitter. This research highlights the importance of developing solutions to detect image forgery in social media and the potential of combining pre-processing with frequency-based methods to improve results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The datasets generated and analysed during this study are available in the GitHub repository: https://github.com/nooratikah/CMF-Images-on-Social-Media_Dataset.

References

  1. Abd NB, Warif AWA, Wahab MYI, Idris R, Salleh, Othman F (2017) SIFT-Symmetry: A robust detection method for copy-move forgery with reflection attack. J Vis Commun Image Represent 46:219–232. https://doi.org/10.1016/j.jvcir.2017.04.004

  2. Abd NB, Warif MYI, Idris AW, Abdul Wahab R, Salleh, Ismail A (2019) CMF-iteMS: An automatic threshold selection for detection of copy-move forgery. Forensic Sci Int 295:83–99. https://doi.org/10.1016/j.forsciint.2018.12.004

  3. Abdalla Y, Iqbal MT, Shehata M (2019) Convolutional neural network for copy-move forgery detection. Symmetry 11(10):Art. no. 10. https://doi.org/10.3390/sym11101280

  4. Abir M, Warif A, Zainal (2021) An evaluation of patch match-based Copy-Move Forgery Detection (CMFD) on social media images. IEEE Conference Publication | IEEE Xplore. https://ieeexplore.ieee.org/document/9573571 . Accessed 14 Aug 2022

  5. Amerini I, Ballan L, Caldelli R, Del Bimbo A, Serra G (2011) A SIFT-based forensic method for copy–move attack detection and transformation recovery. IEEE Trans Inf Forensic Secur 6(3):1099–1110. https://doi.org/10.1109/TIFS.2011.2129512

  6. Ammour N, Alhichri H, Bazi Y, Benjdira B, Alajlan N, Zuair M (2017) Deep Learning Approach for Car Detection in UAV Imagery. Remote Sens 9(4):Art. no. 4. https://doi.org/10.3390/rs9040312

  7. Ardizzone E, Bruno A, Mazzola G (2015) Copy–Move forgery detection by matching triangles of keypoints. IEEE Trans Inform Forensic Secur 10(10):2084–2094. https://doi.org/10.1109/TIFS.2015.2445742

  8. Bansal (2019) Image forgery detection and localization using DCT-based forensic analysis approach. Int J Adv Sci Technol 28(18):15

    Google Scholar 

  9. Barnes C, Goldman DB, Shechtman E, Finkelstein A (2011) The PatchMatch randomized matching algorithm for image manipulation. Commun ACM 54(11):103–110. https://doi.org/10.1145/2018396.2018421

  10. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-Up Robust Features (SURF). Comput Vis Image Underst 110(3):346–359. https://doi.org/10.1016/j.cviu.2007.09.014

  11. Bayram S, Taha Sencar H, Memon N (2009) An efficient and robust method for detecting copy-move forgery. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan, Apr. 2009, pp 1053–1056. https://doi.org/10.1109/ICASSP.2009.4959768

  12. Bhatti UA et al (2020) Geometric algebra applications in geospatial artificial intelligence and remote sensing image processing. IEEE Access 8:155783–155796. https://doi.org/10.1109/ACCESS.2020.3018544

    Article  Google Scholar 

  13. Bhatti UA et al (2020) Hybrid watermarking algorithm using Clifford Algebra with Arnold scrambling and chaotic encryption. IEEE Access 8:76386–76398. https://doi.org/10.1109/ACCESS.2020.2988298

    Article  Google Scholar 

  14. Chen J, Liao X, Qin Z (2021) Identifying tampering operations in image operator chains based on decision fusion. Sig Process Image Commun 95:116287. https://doi.org/10.1016/j.image.2021.116287

  15. Chen J, Liao X, Wang W, Qian Z, Qin Z, Wang Y (2023) SNIS: A Signal Noise Separation-Based Network for Post-Processed Image Forgery Detection. IEEE Trans Circ Syst Video Technol 33(2):935–951. https://doi.org/10.1109/TCSVT.2022.3204753

  16. Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy–move forgery detection. IEEE Trans Inform Forensic Secur 10(11):2284–2297. https://doi.org/10.1109/TIFS.2015.2455334

  17. Elaskily MA et al (2020) A novel deep learning framework for copy-moveforgery detection in images. Multimedia Tools Appl 79(27–28):Art. no. 27–28. https://doi.org/10.1007/s11042-020-08751-7

  18. FotoForensics (2022) https://fotoforensics.com/. Accessed 12 Aug 2022

  19. Fridrich J, Soukal D, Lukáš J. Detection of copy-move forgery in digital images, p 10

  20. Gani G, Qadir F (Oct. 2020) A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata. J Inform Secur Appl 54:102510. https://doi.org/10.1016/j.jisa.2020.102510

  21. Goel N, Kaur S, Bala R (2021) Dual branch convolutional neural network for copy move forgery detection. IET Image Process 15(3):Art. no. 3. https://doi.org/10.1049/ipr2.12051

  22. Hochman N (2014) The social media image. Big Data Soc 1(2):205395171454664. https://doi.org/10.1177/2053951714546645

  23. Huang H, Guo W, Zhang Y (2008) Detection of copy-move forgery in digital images using SIFT algorithm. In: 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application. Wuhan, China, pp 272–276. https://doi.org/10.1109/PACIIA.2008.240.

  24. Isaac MM, Wilscy M (2015) Copy-move forgery detection based on Harris Corner points and BRISK. In: Proceedings of the Third International Symposium on Women in Computing and Informatics - WCI ’15, Kochi, India, pp 394–399. https://doi.org/10.1145/2791405.2791453

  25. Ismail A, Ahmad SA, Soh AC, Hassan K, Harith HH (2019) Improving Convolutional Neural Network (CNN) architecture (miniVGGNet) with Batch Normalization and Learning Rate Decay Factor for Image Classification. Int J Integr Eng 11(4):Art. no. 4. Accessed 06 Mar 2023. [Online]. Available: https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/4558

  26. Jaiswal AK, Gupta D, Srivastava R (2020) Detection of copy-move forgery using hybrid approach of DCT and BRISK. In: 7th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, Feb. 2020, pp 471–476. https://doi.org/10.1109/SPIN48934.2020.9071015

  27. Langille A, Gong M (2006) An efficient match-based duplication detection algorithm. In: The 3rd Canadian Conference on Computer and Robot Vision (CRV’06), Quebec, Canada, pp 64–64. https://doi.org/10.1109/CRV.2006.9

  28. Li Y (Jan. 2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224:1–3. https://doi.org/10.1016/j.forsciint.2012.10.031

  29. Li Y, Zhou J (May 2019) Fast and effective Image copy-move forgery detection via hierarchical feature point matching. IEEE Trans Inform Forensic Secur 14(5):1307–1322. https://doi.org/10.1109/TIFS.2018.2876837

  30. Li S, Zhang Z, Li B, Li C (2018) Multiscale rotated bounding box-based deep learning method for detecting ship targets in remote sensing images. Sensors 18(8):Art. no. 8. https://doi.org/10.3390/s18082702

  31. Liao X, Li K, Zhu X, Liu KJR (2020) Robust detection of image operator chain with two-stream convolutional neural network. IEEE J Sel Top Signal Process 14(5):955–968. https://doi.org/10.1109/JSTSP.2020.3002391

  32. Mahdian B, Saic S (2007) Detection of copy–move forgery using a method based on blur moment invariants. Forensic Sci Int 171(2–3):180–189. https://doi.org/10.1016/j.forsciint.2006.11.002

  33. Meena KB, Tyagi V (2020) A hybrid copy-move image forgery detection technique based on Fourier-Mellin and scale invariant feature transforms. Multimed Tools Appl 79(11–12):8197–8212. https://doi.org/10.1007/s11042-019-08343-0

  34. Mishra P, Mishra N, Sharma S, Patel R (2013) Region duplication forgery detection technique based on SURF and HAC. Sci World J:1–8. https://doi.org/10.1155/2013/267691

  35. Otsu N (Jan. 1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66. https://doi.org/10.1109/TSMC.1979.4310076

  36. Picture Thresholding Using an Iterative Selection Method | IEEE Journals & Magazine | IEEE Xplore. https://ieeexplore.ieee.org/document/4310039. Accessed 06 Mar 2023

  37. Rao Y, Ni J (2016) A deep learning approach to detection of splicing and copy-move forgeries in images. In: 2016 IEEE International Workshop on Information Forensics and Security (WIFS), Abu Dhabi, United Arab Emirates, Dec. pp 1–6. https://doi.org/10.1109/WIFS.2016.7823911

  38. Tralic D, Zupancic I, Grgic S, Grgic M (2013) CoMoFoD - New database for copy-move forgery detection. The International Symposium ELMAR, p 6

  39. Ustubioglu B, Ulutas G, Ulutas M, Nabiyev VV (2016) A new copy move forgery detection technique with automatic threshold determination. AEU - Int J Electron Commun 70(8):1076–1087. https://doi.org/10.1016/j.aeue.2016.05.005

  40. Zanardelli M, Guerrini F, Leonardi R, Adami N (2022) Image forgery detection: a survey of recent deep-learning approaches. Multimed Tools Appl Oct. https://doi.org/10.1007/s11042-022-13797-w

    Article  Google Scholar 

  41. Zheng J, Liu Y, Ren J, Zhu T, Yan Y, Yang H (2016) Fusion of block and keypoints based approaches for effective copy-move image forgery detection. Multidim Syst Sign Process 27(4):989–1005. https://doi.org/10.1007/s11045-016-0416-1

  42. Zhu Y, Shen X, Chen H (2016) Copy-move forgery detection based on scaled ORB. Multimed Tools Appl 75(6):3221–3233. https://doi.org/10.1007/s11042-014-2431-2

Download references

Acknowledgements

This research was supported by Ministry of Higher Education (MOHE) through Fundamental Research Grant Scheme (FRGS/1/2020/ICT04/UTHM/02/1).

Author information

Authors and Affiliations

  1. Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Noor Atikah Mat Abir

  2. Centre for Information Security Research, Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Nor Bakiah Abd Warif

  3. Applied Information System Focus Group, Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Nurezayana Zainal

Authors
  1. Noor Atikah Mat Abir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nor Bakiah Abd Warif
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nurezayana Zainal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nor Bakiah Abd Warif.

Ethics declarations

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abir, N.A.M., Warif, N.B.A. & Zainal, N. An automatic enhanced filters with frequency-based copy-move forgery detection for social media images. Multimed Tools Appl 83, 1513–1538 (2024). https://doi.org/10.1007/s11042-023-15506-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15506-7

Keywords

Search

Navigation