Skip to main content
SpringerLink
Log in

Ciratefi based copy move forgery detection on digital images

  • 1169: Interdisciplinary Forensics: Government, Academia and Industry Interaction
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

There is an increase in the requirement of digital image authentication in law, journalism, and medicine, even in the industry. Copy move forgery is the most common method of forgery methods which are applied to digital images. The importance of verifying digital images that are used in important areas with real-time systems is increasing. Taking this need into consideration, in this study, a robust digital image copy move forgery detection method is proposed to realize in real time. The proposed method first extracts the textural form of the input image. The SIFT keypoints and descriptors are obtained from textual images thus more robust keypoints and descriptors will be utilized. Keypoint matching is realized to reveal the image is forged or not and suspicious regions are determined. The localization of forged pixel is realized via Ciratefi based approach. The post-processing step is applied to make the labeling pixels more accurate by utilizing Connected Component Labeling and morphological operation. The GRIP and CMH datasets are used to showing the effectiveness of the state-of-the-art and proposed method. The method is robust to geometric distortion attacks and image degradation attacks. The results indicate that the proposed method has the highest performance especially against geometric distortion attacks suck as rotation and scaling.

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26

Similar content being viewed by others

References

  1. Abdel-Basset M, Manogaran G, Fakhry AE, El-Henawy I (2018) 2-levels of clustering strategy to detect and locate copy-move forgery in digital images. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6266-0

    Article  Google Scholar 

  2. Amerini I, Ballan L, Caldelli R, Bimbo AD, Serra G (2011) A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans Inf Forensics Secur 6(3):1099–1110

    Article  Google Scholar 

  3. Amerini I, Ballan L, Caldelli R, Del Bimbo A, Del Tongo L, Serra G (2013) Copy-move forgery detection and localization by means of robust clustering with J-linkage. Signal Process 28(6):659–1669

    Google Scholar 

  4. Arau´jo SA, Kim HY (2011) Ciratefi: an RST-invariant template matching with extension to color images. Integr Comput Aided Eng 18(1):75–90

    Article  Google Scholar 

  5. Bayram S, Sencar HT, Memon N (2009) An efficient and robust method for detecting copy-move forgery. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, Apr 2009, New York, pp 1053–1056.

  6. Bravo-Solorio S, Nandi AK (2011) Exposing duplicated regions affected by reflection, rotation and scaling. In: Proceedings of the international conference on acoustics, speech and signal processing, May 2011, Prague, pp 1880–1883.

  7. Bi X, Pun C-M (2017) Fast reflective offset-guided searching method for copy-move forgery detection. Inf Sci 418–419:531–545

    Article  Google Scholar 

  8. Bi X, Pun CM (2018) Fast copy-move forgery detection using local bidirectional coherency error refinement. Pattern Recogn 81:161–175

    Article  Google Scholar 

  9. Bi X, Pun C, Yuan X (2016) Multi-level dense descriptor and hierarchical feature matching for copy-move forgery detection. Inf Sci 345:226–242

    Article  Google Scholar 

  10. Bo X, Junwen W, Guangjie L, Yuewei D (2010) Image copy-move forgery detection based on SURF. In: Proceedings of the international conference of multimedia Information networking and security (MINES), Nov 2010, pp 889–892.

  11. Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensics Secur 7:1841–1854

    Article  Google Scholar 

  12. Cozzolino D, Poggi G, Verdoliva L (2014) Copy-move forgery detection based on patchmatch. In: Proceedings of the IEEE international conference on image processing, Paris, 27–30 Oct 2014.

  13. Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy-move forgery detection. IEEE Trans Inf Forensics Secur 10(11):2284–2297

    Article  Google Scholar 

  14. Emam M, Han Q, Niu X (2016) PCET based copy-move forgery detection in images under geometric transforms. Multimed Tools Appl 75(18):11513–11527

    Article  Google Scholar 

  15. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  16. Fridrich AJ, Soukal BD, Lukáš AJ (2003) Detection of copy-move forgery in digital images. In: Proceedings of the digital forensic research workshop (DFRWS).

  17. Gani G, Qadir F (2019) A novel method for digital image copy-move forgery detection and localization using evolving cellular automata and local binary patterns. Envolving Syst. https://doi.org/10.1007/s12530-019-09309-1

    Article  Google Scholar 

  18. Haralick RM, Shapiro L (1992) “Connected component labeling”, computer and robot vision, vol 1. Addison-Wesley, New York

    Google Scholar 

  19. Huang Y, Lu W, Sun W, Long D (2011) Improved DCT based detection of copy move forgery in images. Forensic Sci Int 206:178–184

    Article  Google Scholar 

  20. Korus P (2017) Digital image integrity—a survey of protection and verification techniques. Digit Signal Process 71:1–26

    Article  MathSciNet  Google Scholar 

  21. Kundur D, Hatzinakos D (1999) Digital watermarking for telltale tamper proofing and authentication. Proc IEEE 87(7):1167–1180

    Article  Google Scholar 

  22. Li Y, Zhou J (2019) Fast and effective image copy-move forgery detection via hierarchical feature point matching. IEEE Trans Inf Forensics Secur. https://doi.org/10.1109/TIFS.2018.2876837

    Article  Google Scholar 

  23. Li J, Li X, Yang B (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518

    Article  Google Scholar 

  24. Lian S, Kanellopoulos D (2009) Recent advances in multimedia information system security. Informatica 33:3–24

    MathSciNet  Google Scholar 

  25. Luo W, Huang J, Qiu G (2009) Robust detection of region-duplication forgery in digital images, vol 4. In: Proceedings of the international conference on pattern recognition, Nov 2009, Hong Kong, pp 746–749.

  26. Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the international conference on computer vision, Sep 1999, Kerkyra, pp 1150–1157.

  27. Mahdian B, Saic S (2007) Detection of copy-move forgery using a method based on blur moment invariants. Forensic Sci Int 171:180–189

    Article  Google Scholar 

  28. Muzaffer G, Ulutas G (2017) A fast and effective digital image copy move forgery detection with binarized SIFT. In: Proceedings of the TSP, Barcelona, pp 595–598.

  29. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  30. Om A, Be K (2013) Passive detection of copy-move forgery in digital images: state-of-the-art. Forensic Sci Int 231:284–295

    Article  Google Scholar 

  31. Popescu A, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions, Dartmount Collage, Tech. Rep. TR2004-515.

  32. Pun CM, Chung JL (2018) A two-stage localization for copy-move forgery detection. Inf Sci (N Y) 463–464:33–55

    Article  MathSciNet  Google Scholar 

  33. Pun C, Member S, Yuan X, Bi X (2015) Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Trans Inf Forensics Secur 10:1705–1716

    Article  Google Scholar 

  34. Rey C, Dugelay JL (2002) A survey of watermarking algorithms for image authentication. EURASIP J Appl Signal Process 2002(6):613–621

    MATH  Google Scholar 

  35. Rey C, Dugelay JL (2002) A survey of watermarking algorithms for image authentication. EURASIP J Appl Signal Process. https://doi.org/10.1155/S1110865702204047

    Article  MATH  Google Scholar 

  36. Ryu S, Kirchner M, Lee M, Lee H (2013) Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Trans Inf Forensics Secur 8:1355–1370

    Article  Google Scholar 

  37. Silva E, Carvalho T, Ferreira A, Rocha A (2015) Going deeper into copy-move forgery detection: exploring image telltales via multi-scale analysis and voting processes. J Vis Commun Image Represent 29:16–32

    Article  Google Scholar 

  38. Ulutas G, Ustubioglu A, Ustubioglu B, Nabiyev VV, Ulutas M (2017) Medical image tamper detection based on passive image authentication. J Digit Imaging 30(6):695–709

    Article  Google Scholar 

  39. Ustubioglu B, Muzaffer G, Ulutas G, Nabiyev V, Ulutas M (2015) A novel keypoint based forgery detection method based on local phase quantization and SIFT. In Proceedings of the ELECO, Nov 2015, pp 185–189.

  40. Ustubioglu B, Baykal E, Muzaffer G, Ulutas G (2016) Blur invariant image forgery detection method using local phase quantization. J Energy Power Eng 10(6):358–363

    Google Scholar 

  41. Wenchang S, Fei Z, Bo Q, Bin L (2016) Improving image copy-move forgery detection with particle swarm optimization techniques. Proc China Commun 13(1):139–149

    Article  Google Scholar 

  42. Yang F, Li J, Lu W, Weng J (2019) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intell 59:73–83

    Article  Google Scholar 

  43. Yang H-Y, Qi S-R, Niu Y, Niu P-P, Wang X-Y (2019) Copy-move forgery detection based on adaptive keypoints extraction and matching. Multimed Tools Appl 78(24):34585–34612

    Article  Google Scholar 

  44. Zandi M, Mahmoudi-Aznaveh A, Talebpour A (2016) Iterative copy-move forgery detection based on a new interest point detector. Trans Inf Forensics Secur. https://doi.org/10.1109/TIFS.2016.2585118

    Article  Google Scholar 

  45. Zhu Y, Shen X, Chen H (2015) Copy-move forgery detection based on scaled ORB. Multimed Tools Appl 75(6):1–15

    Google Scholar 

Download references

Funding

This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with Project No: 119E045.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

    Gul Tahaoglu, Guzin Ulutas, Beste Ustubioglu, Mustafa Ulutas & Vasif V. Nabiyev

Authors
  1. Gul Tahaoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guzin Ulutas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Beste Ustubioglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mustafa Ulutas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vasif V. Nabiyev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gul Tahaoglu.

Ethics declarations

Conflict of interest

The authors declare that this article is original, has not been published before, and is not currently being considered for publication elsewhere and there is no conflict of interest regarding the publication. The authors confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. The authors further confirm that the order of authors listed in the manuscript has been approved by all of them.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tahaoglu, G., Ulutas, G., Ustubioglu, B. et al. Ciratefi based copy move forgery detection on digital images. Multimed Tools Appl 81, 22867–22902 (2022). https://doi.org/10.1007/s11042-021-11503-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11503-w

Keywords

Search

Navigation