Skip to main content
SpringerLink
Log in

A robust forgery detection algorithm for object removal by exemplar-based image inpainting

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

Abstract

Object removal is a malicious image forgery technique, which is usually achieved by exemplar-based image inpainting in a visually plausible way. Most existing forgery detection approaches utilize similar block pairs between inpainted area and the rest areas, but they invalidate when those inpainted images are further subjected to some post-processing operations such as JPEG compression, Gaussian noise addition and blurring. It is desirable to develop a forensic method which is robust to object removal with post-processing. From some preliminary experiments, we observe that post-processing destroys the similarity of block pairs and simultaneously disturbs the correlations among adjacent pixels to some extent. Inspired by the strong ability of joint probability density matrix (JPDM) in characterizing such correlation, we propose a hybrid forensics strategy. Firstly, our earlier method is employed to detect whether a candidate image is forged or not. Secondly, for those undetected images after the first step, JPDM is computed for each difference array to model the correlations among adjacent DCT coefficients, and the average of these matrixes are computed as feature vectors to further expose tampering traces. Experimental results show that the proposed approach can effectively detect object removal by exemplar-based inpainting either with or without post-processing.

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

Similar content being viewed by others

Notes

  1. Available at http://dde.binghamton.edu/download/ensemble/.

  2. Available at http://homepages.lboro.ac.uk/cogs/datasets/ucid/ucid.html.

  3. Available at http://yokoya.naist.jp/research2/inpainting/.

References

  1. Amerini I, Ballan L, Caldelli R et al (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. Bacchuwar KS, Ramakrishnan K R (2013) A jump patch-block match algorithm for multiple forgery detection. In: Proc. of IEEE international multi-conference on automation, computing, communication, control and compressed sensing (iMac4s), pp 723–728

  3. Birajdar G K, Mankar V H (2013) Digital image forgery detection using passive techniques: A survey. Digit Investig 10(3):226–245

    Article  Google Scholar 

  4. Chang I, Yu J, Chang C C (2013) A forgery detection algorithm for exemplar-based inpainting images using multi-region relation. Image Vis Comput 31 (1):57–71

    Article  MathSciNet  Google Scholar 

  5. Cozzolino D, Gragnaniello D, Verdoliva L (2014) Image forgery detection through residual-based local descriptors and block-matching. In: IEEE International conference on image processing (ICIP), pp 5297–5301

  6. Criminisi A, Prez P, Toyama K (2004) Region filling and object removal by exemplar-based image inpainting. IEEE Trans Image Process 13(9):1200–1212

    Article  Google Scholar 

  7. Gu B, Sheng V S, Wang Z et al (2015) Incremental learning for v-support vector regression. Neural Netw 67:140–150

    Article  Google Scholar 

  8. Gu B, Sheng V S, Tay K Y et al (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416

    Article  MathSciNet  Google Scholar 

  9. Guillemot C, Meur O L (2014) Image inpainting: overview and recent advances. IEEE Signal Process Mag 31(1):127–144

    Article  Google Scholar 

  10. He Z, Lu W, Sun W et al (2012) Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recog 45(12):4292–4299

    Article  Google Scholar 

  11. Kakar P, Sudha N (2012) Exposing postprocessed copy-paste forgeries through transform-invariant features. IEEE Trans Inf Forens Secur 7(3):1018–1028

    Article  Google Scholar 

  12. Kirchner M, Fridrich J (2010) On detection of median filtering in digital images. SPIE Electron Imag Int Soc Opt Photon 754110–754110

  13. Kodovsky J, Fridric J, Holub V (2012) Ensemble classifiers for steganalysis of digital media. IEEE Trans Inf Forens Secur 7(2):432C444

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Liang Z, Yang G, Ding X et al (2015) An efficient forgery detection algorithm for object removal by exemplar-based image inpainting. J Vis Commun Image Represent 30:75–85

    Article  Google Scholar 

  16. Liu Q, Chen Z (2014) Improved approaches with calibrated neighboring joint density to steganalysis and seam-carved forgery detection in JPEG images. ACM Trans Intell Syst Technol 5(4):63

    Google Scholar 

  17. Muhammad G, Hussain M, Bebis G (2012) Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digit Investig 9(1):49–57

    Article  Google Scholar 

  18. Stamm M C, Wu M, Liu K J R (2013) Information forensics: an overview of the first decade. IEEE Access 1:167–200

    Article  Google Scholar 

  19. Shi Y Q, Chen C, Xuan G et al (2007) Steganalysis versus splicing detection. Int Workshop Digit Watermark 158–172

  20. Wang J, Lu K, Pan D et al (2014) Robust object removal with an exemplar-based image inpainting approach. Neurocomputing 123:150–155

    Article  Google Scholar 

  21. Wu Q, Sun S J, Zhu W et al (2008) Detection of digital doctoring in exemplar-based inpainted images. In: Proc. of IEEE international conference on machine learning and cybernetics, vol 3, pp 1222–1226

  22. Xia Z H, Wang X H, Sun X M, Wang B W (2014) Steganalysis of least significant bit matching using multi-order differences. Secur Commun Netw 7 (8):1283–1291

    Article  Google Scholar 

  23. Xia Z, Wang X, Sun X et al (2016) Steganalysis of LSB matching using differences between nonadjacent pixels. Multimed Tools Appl 75(4):1947–1962

    Article  Google Scholar 

  24. Xia Z, Wang X, Zhang L et al (2016) A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans Inf Forens Secur 11(11):2594–2608

    Article  Google Scholar 

  25. Zhao X, Wang S, Li S et al (2013) Image splicing detection based on noncausal markov model. In: IEEE International conference on image processing, pp 4462–4466

Download references

Acknowledgements

We would like to thank the anonymous reviewers for their professional comments and valuable suggestions. This work is partially or fully sponsored by National Natural Science Foundation of China (61572183, 61379143), the Specialized Research Fund for the Doctoral Program of Higher Education (20120161110014), the Scientific Research Fund of Hunan Provincial Education Department of China (14C0029), Natural Science Foundation of Hunan Province (2016JJ2005). The authors appreciate the nice help from Mr Moses Odero for his improving the English usages.

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Dengyong Zhang, Zaoshan Liang & Gaobo Yang

  2. School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha, 410005, China

    Dengyong Zhang

  3. College of Mathematics and Economics, Hunan University, Changsha, 410082, China

    Qingguo Li

  4. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 2211166, China

    Leida Li

  5. School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Xingming Sun

Authors
  1. Dengyong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zaoshan Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gaobo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qingguo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Leida Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xingming Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gaobo Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, D., Liang, Z., Yang, G. et al. A robust forgery detection algorithm for object removal by exemplar-based image inpainting. Multimed Tools Appl 77, 11823–11842 (2018). https://doi.org/10.1007/s11042-017-4829-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4829-0

Keywords

Search

Navigation