SpringerLink
Log in

A passive authentication scheme for copy-move forgery based on package clustering algorithm

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

Abstract

Copy-move forgery as one of popular methods is widely used to tamper digital images. Passive authentication is extensively used to detect the copy-move forgery images. This paper proposes a passive authentication scheme for copy-move forgery based on the discrete cosine transform (DCT) and the package clustering algorithm. The copy regions and paste regions can be automatically detected in doctored digital images. This scheme works by first applying the DCT to small fixed image blocks to obtain their features and the size of feature vectors are reduced. Moreover, the package clustering algorithm is applied to replace the general lexicographic order technologies to improve the detection precision. The similar blocks can be found by comparing the feature vectors in each package. The experimental results represent that the proposed scheme can locate irregular and meaningful tampered regions and multiply duplicated regions in a suspicious image. The duplicated regions can also be located in digital images that are distorted by adding white Gaussian noise, Gaussian blurring and their mixed operations.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

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

Similar content being viewed by others

References

  1. 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. In: Information Forensics and Security, IEEE Transactions on. vol. 6, no. 3, pp 1099–1110

  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 Forensics Secur 6(3):1099–1110

    Article  Google Scholar 

  3. Beste U, Gyzin U, Mustafa U, Vasif N, Arda U (2015) LBP-DCT based copy move forgery detection algorithm. In: Information Sciences and Systems, vol. 363, pp 127–136

  4. Bi XL, Pun CM, Yuan CX (2016) Multi-level dense descriptor and hierarchical feature matching for copy-move forgery detection. Inf Sci 345(1):226–242

    Article  Google Scholar 

  5. Christlein V, Riess C, Jordan J, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensics Secur 7(6):1841–1854

    Article  Google Scholar 

  6. Fridrich J, Soukalm D, Lukas J (2003) Detection of copy-move forgery in digital images. Digital Forensic Research workshop, Cleveland, pp 19–23

  7. Huang YP, Lu W, Sun W, Long DY (2011) Improved DCT-based detection of copy-move forgery in images. Forensic Sci Int 206(1–3):178–184

    Article  Google Scholar 

  8. Kang L, Cheng XP, Li K (2010) Copy-move forgery detection in digital image. In: Image and Signal Processing (CISP), vol. 5, pp 2419–2421

  9. Kodak Lossless True Color Image: http://r0k.us/graphics/kodak/

  10. Kumar S, Desai J, Mukherjee S (2014) DCT-PCA based method for copy-move forgery detection. Adv Intell Syst Comput 249:577–583

    Google Scholar 

  11. Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224(1):59–67

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

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

    MathSciNet  Google Scholar 

  14. Mahmoud E, Han Q, Niu XM (2015) PCET based copy-move forgery detection in images under geometric transforms. Multimed Tools Appl 1–15

  15. Manu VT, Mehtre BM (2016) Detection of copy-move forgery in images using segmentation and SURF. Proc Second Int Symp Signal Process Intell Recogn Syst 425:645–654

    Google Scholar 

  16. Mishra A, Goel A, Singh R, Chelty G, Singh L (2012) A novel image watermarking scheme using extreme learning machine. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp 1–6

  17. Ng T-T, Chang S-F, Hsu J, Pepeljugoski M (2004) Columbia photographic images and photorealistic computer graphics dataset ADVENT. Columbia Univ., New York, Tech. Rep

  18. Popscu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions, Tech. Rep. TR2003-515, Dartmouth College

  19. Rani S, Jayamohan M, Sruthy S (2016) Scale invariant detection of copy-move forgery using fractal dimension and singular values. Adv Signal Process Intell Recogn Syst 425:559–570

    Article  Google Scholar 

  20. Sebe N, Liu Y, Zhuang Y, Huang T, Chang SF (2007) Blind passive Madia forensics: motivation and opportunity, multimedia conten analysis and mining, vol. 4577. Springer, Berlin/Heidelberg, pp 57–59

  21. Shabnam MS, Mohsen EM, Mohsen M (2015) A new approach for copy-move detection based on improved weber local descriptor. In: Journal of Forensic Sciences

  22. The Columbia image splicing detection evaluation database: http://www.ee.columabia.edu.cn/ln/dvmm/downloads/AuthSpliceDataset

  23. Tong X, Liu Y, Zhang M, Chen Y (2013) A novel chaos-based fragile watermarking for image tampering detection and self-recovery. Signal Process Image Commun 28:301–308

    Article  Google Scholar 

  24. Yu J, Han QL (2015) Detection of copy-move forgery in digital images using discrete cosine transform and cluster analysis. In: International Conference on Computer Networks and Information Security (CNIS), pp 73–76

  25. Zhou L, Wang D, Guo Y, Zhang J (2007) Blur detection of digital forgery using mathematical morphology. In: Proceedings of the 1st KES International Symposium on Agent and Multi-Agent systems. Technologies and Applications, Springer-verlag, Wroclaw, Poland

  26. Zimba M, **ngming S (2011) DWT-PCA (EVD) based copy-move image forgery detection. Int J Digit Content Technol Appl 5(1):251–258

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (NSFC) under the grant Nos. U1536110, 61373180.

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, People’s Republic of China

    Huan Wang, Hong-**a Wang & Qing Qian

  2. School of Computer & Software, Nan**g University of Information Science & Technology, Nan**g, Jiangsu, China, 210044

    **ng-Ming Sun

Authors
  1. Huan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong-**a Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. **ng-Ming Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qing Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong-**a Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Wang, HX., Sun, XM. et al. A passive authentication scheme for copy-move forgery based on package clustering algorithm. Multimed Tools Appl 76, 12627–12644 (2017). https://doi.org/10.1007/s11042-016-3687-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3687-5

Keywords

Search

Navigation