Skip to main content
SpringerLink
Log in

Efficient PatchMatch Algorithm to Detect False Crowd in Digitally Forged Images

  • Conference paper
  • First Online:
Intelligent Technologies and Applications (INTAP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1198))

Included in the following conference series:

  • 902 Accesses

Abstract

The authenticity and reliability of digital images have become one of the major concerns recently due to the ease in manipulating and modifying these images. Similar manipulation in crowded images gives rise to the false crowd, where a person or group of persons is copied and pasted in the same image. Thus, the detection of such false crowds is the focus of current research. In this paper, false crowd detection in forged images is carried out using modified and improved PatchMatch algorithm which can even detect multiple copies of the same instance. To sperate humans from non-human objects a human detection algorithm is used in the post-processing phase. A benchmark database consisting of false crowd images has also been developed. Experimental results confirm that the technique is capable of detecting the false crowds successfully and even robust for multiple cloning problem.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Raje, N., Dinakar, C.: Overview of immunodeficiency disorders. Immunol. Allergy Clin. 35, 599–623 (2015)

    Article  Google Scholar 

  2. Pan, X., Lyu, S.: Detecting image region duplication using SIFT features. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1706–1709 (2010)

    Google Scholar 

  3. Pan, X., Lyu, S.: Region duplication detection using image feature matching. In: IEEE Transactions on Information Forensics and Security, pp. 857–867 (2010)

    Google Scholar 

  4. Shivakumar, B.L., Baboo, S.S.: Detection of region duplication forgery in digital images using SURF. Int. J. Comput. Sci. Issues (IJCSI) 8, 199 (2011)

    Google Scholar 

  5. Guo, J.M., Liu, Y.F., Wu, Z.J.: Duplication forgery detection using improved DAISY descriptor. Expert Syst. Appl. 40, 707–714 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Qazi, T., et al.: Survey on blind image forgery detection. IET Image Process. 7, 660–670 (2013)

    Article  Google Scholar 

  9. Warif, N.B., et al.: Copy-move forgery detection: survey, challenges and future directions. J. Netw. Comput. Appl. 75, 259–278 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Cao, Y., Gao, T., Fan, L., Yang, Q.: A robust detection algorithm for copy-move forgery in digital images. Forensic Sci. Int. 214, 33–43 (2012)

    Article  Google Scholar 

  12. Muhammad, G., Hussain, M., Bebis, G.: Passive copy move image forgery detection using undecimated dyadic wavelet transform. Dig. Investigat. 9, 49–57 (2012)

    Article  Google Scholar 

  13. Hashmi, M.F., Anand, V., Keskar, A.G.: Copy-move image forgery detection using an efficient and robust method combining un-decimated wavelet transform and scale invariant feature transform. Aasri Procedia 9, 84–91 (2014)

    Article  Google Scholar 

  14. Zhao, J., Guo, J.: Passive forensics for copy-move image forgery using a method based on DCT and SVD. Forensic Sci. Int. 233, 158–166 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Wu, Q., Wang, S., Zhang, X.: Log-polar based scheme for revealing duplicated regions in digital images. IEEE Signal Process. Lett. 18, 559–562 (2011)

    Article  Google Scholar 

  18. Christlein, V., Riess, C., Angelopoulou, E.: On rotation invariance in copy-move forgery detection. In: 2010 IEEE International Workshop on Information Forensics and Security, pp. 1–6 (2010)

    Google Scholar 

  19. Nguyen, D.T., Li, W., Ogunbona, P.O.: Human detection from images and videos: a survey. Patt. Recogn. 51, 148–175 (2016)

    Article  Google Scholar 

  20. Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. (ToG) 1, 24 (2009)

    Google Scholar 

  21. Barnes, C., Shechtman, E., Goldman, D.B., Finkelstein, A.: The generalized patchmatch correspondence algorithm. In European Conference on Computer Vision, pp. 29–43 (2010)

    Google Scholar 

  22. Papageorgiou, C., Poggio, T.: A trainable system for object detection. Int. J. Comput. Vis. 38, 15–33 (2000)

    Article  Google Scholar 

  23. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: International Conference on Computer Vision and Pattern Recognition (CVPR 2005), pp. 886–893 (2005)

    Google Scholar 

  24. Gavrila, D.M.: A bayesian exemplar-based approach to hierarchical shape matching. IEEE Trans. Pattern Anal. Mach. Intell. 29, 8 (2007)

    Article  Google Scholar 

  25. Gao, W., Ai, H., Lao, S.: Adaptive contour features in oriented granular space for human detection and segmentation. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1786–1793 (2009)

    Google Scholar 

  26. Maji, S., Berg, A.C., Malik, J.: Efficient classification for additive kernel SVMs. IEEE Trans. Pattern Anal. Mach. Intell. 35, 66–77 (2013)

    Article  Google Scholar 

  27. Dollár, P., Babenko, B., Belongie, S., Perona, P., Tu, Z.: Multiple component learning for object detection. In European Conference on Computer Vision, pp. 211–224 (2008)

    Google Scholar 

  28. Dollár, P., Belongie, S.J., Perona, P.: The fastest pedestrian detector in the west. In: BMVC, p. 7 (2010)

    Google Scholar 

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

    Article  Google Scholar 

  30. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Signal, National University of Sciences and Technology NUST, Islamabad, Pakistan

    Rakhshanda Javid, Abdul Ghafoor & Naveed Iqbal

  2. COMSATS University, Islamabad, Pakistan

    M. Mohsin Riaz

Authors
  1. Rakhshanda Javid
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Mohsin Riaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdul Ghafoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Naveed Iqbal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rakhshanda Javid .

Editor information

Editors and Affiliations

  1. Islamia University of Bahawalpur, Baghdad, Pakistan

    Imran Sarwar Bajwa

  2. Metropolitan University, Belgrade, Serbia

    Tatjana Sibalija

  3. University of Technology Malaysia, Johor Bahru, Malaysia

    Dayang Norhayati Abang Jawawi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Javid, R., Riaz, M.M., Ghafoor, A., Iqbal, N. (2020). Efficient PatchMatch Algorithm to Detect False Crowd in Digitally Forged Images. In: Bajwa, I., Sibalija, T., Jawawi, D. (eds) Intelligent Technologies and Applications. INTAP 2019. Communications in Computer and Information Science, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-5232-8_53

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-5232-8_53

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5231-1

  • Online ISBN: 978-981-15-5232-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation