Skip to main content
SpringerLink
Log in

Feature point-based copy-move forgery detection: covering the non-textured areas

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

Abstract

Detection of copy-move forgery has recently attracted much attention. During the past decade, two categories of methods, namely block-based and feature point-based methods, gradually developed. Compared with block-based methods, feature point-based methods exhibit remarkable performance with respect to robustness and computational cost. However, the feature point-based methods are still incomplete especially in terms of forgeries involving small smooth regions. In this paper, we solve this problem by cautiously supplementing redundant feature points and feature fusion. We propose two-stage feature detection to obtain better feature coverage and enhance the matching performance by combining the MROGH and HH descriptor. We evaluated our method on two representative datasets. We use precision, recall and F 1 score to quantitatively evaluate the performance. Experimental results confirm the efficacy of our work.

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

Similar content being viewed by others

References

  1. 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 Image Commun 28(6):659–669

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. Bashar M, Noda K, Ohnishi N, Mori K (2010) Exploring duplicated regions in natural images. IEEE Trans Image Process PP(99):1–1. doi:10.1109/TIP.2010.2046599

    Google Scholar 

  4. Bay H, Tuytelaars T, Van Gool L (2006) Surf: Speeded up robust features. In: Computer vision–ECCV 2006. Springer, pp 404–417

  5. Bayram S, 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, ICASSP 2009, pp 1053–1056. doi:10.1109/ICASSP.2009.4959768, (to appear in print)

  6. Bravo-Solorio S, Nandi A (2011) Exposing duplicated regions affected by reflection, rotation and scaling. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1880–1883. doi:10.1109/ICASSP.2011.5946873, (to appear in print)

  7. Brown M, Szeliski R, Winder S (2005) Multi-image matching using multi-scale oriented patches. In: IEEE computer society conference on computer vision and pattern recognition, CVPR 2005, vol 1, pp 510–517. doi:10.1109/CVPR.2005.235

  8. Chen L, Lu W, Ni J, Sun W, Huang J (2013) Region duplication detection based on harris corner points and step sector statistics. J Vis Commun Image Represent 24(3):244–254

    Article  Google Scholar 

  9. 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(6):1841–1854. doi:10.1109/TIFS.2012.2218597

    Article  Google Scholar 

  10. Fan B, Wu F, Hu Z (2011) Aggregating gradient distributions into intensity orders: A novel local image descriptor. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2377–2384

  11. 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 

  12. Fridrich AJ, Soukal BD, Lukáṡ AJ (2003) Detection of copy-move forgery in digital images. In: Proceedings of digital forensic research workshop. Citeseer

  13. Gkalelis N, Mezaris V (2014) Video event detection using generalized subclass discriminant analysis and linear support vector machines. In: Proceedings of international conference on multimedia retrieval, vol 25. ACM

  14. Guo JM, Liu YF, Wu ZJ (2013) Duplication forgery detection using improved daisy descriptor. Expert Syst Appl 40(2):707–714. doi:10.1016/j.eswa.2012.08.002. http://www.sciencedirect.com/science/article/pii/S0957417412009633

    Article  Google Scholar 

  15. Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Science International 224(1–3):59–67. doi:10.1016/j.forsciint.2012.10.031. http://www.sciencedirect.com/science/article/pii/S0379073812004859

    Article  Google Scholar 

  16. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  17. Luo W, Huang J, Qiu G (2006) Robust detection of region-duplication forgery in digital image. In: 18th International Conference on Pattern recognition, ICPR 2006. IEEE, pp 746–749

  18. Mahdian B, Saic S (2007) Detection of copy–move forgery using a method based on blur moment invariants. Forensic Sci Int 171(2):180–189

    Article  Google Scholar 

  19. Noble JA (1988) Finding corners. Image Vis Comput 6(2):121–128

    Article  Google Scholar 

  20. Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensic Secur 5(4):857–867

    Article  Google Scholar 

  21. Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. Department Computer Science, Dartmouth College, Technical Report. TR2004-515

  22. Ryu SJ, Lee MJ, Lee HK (2010) Detection of copy-rotate-move forgery using zernike moments. In: Bhme R, Fong P, Safavi-Naini R (eds) Information Hiding, Lecture Notes in Computer Science, vol 6387. Springer Berlin Heidelberg, pp 51–65

  23. Scherp A, Mezaris V (2014) Survey on modeling and indexing events in multimedia. Multimedia Tools Appl 70(1):7–23. doi:10.1007/s11042-013-1427-7

    Article  Google Scholar 

  24. Shivakumar B, Baboo LDSS (2011) Detection of region duplication forgery in digital images using surf. IJCSI Int J Comput Sci Issues 8(4)

  25. Tola E, Lepetit V, Fua P (2010) Daisy: An efficient dense descriptor applied to wide-baseline stereo. IEEE Trans Pattern Anal Mach Intell 32(5):815–830

    Article  Google Scholar 

  26. Tuytelaars T (2010) Dense interest points. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2281–2288

  27. Van De Weijer J, Schmid C (2006) Coloring local feature extraction. In: Computer vision–ECCV 2006. Springer, pp 334–348

Download references

Acknowledgments

The authors would like to thank Yuenan Li and Irene Amerini for sharing their copy-move forgery detection code. We also thank Mahmoud Emam and all the anonymous reviewers for their helpful comments and suggestions. Additionally, this work is supported by the National Natural Science Foundation of China (61100187 and 61361166006) and the China Postdoctoral Science Foundation (2011M500666).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China

    Liyang Yu, Qi Han & Xiamu Niu

  2. Department of Computer Science and Technology, Mudanjiang Normal University, Mudanjiang, People’s Republic of China

    Liyang Yu

Authors
  1. Liyang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qi Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiamu Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qi Han.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, L., Han, Q. & Niu, X. Feature point-based copy-move forgery detection: covering the non-textured areas. Multimed Tools Appl 75, 1159–1176 (2016). https://doi.org/10.1007/s11042-014-2362-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-2362-y

Keywords

Search

Navigation