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Copy-move forgery detection technique based on discrete cosine transform blocks features

  • S.I. : Higher Level Artificial Neural Network Based Intelligent Systems
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Abstract

With the increasing number of software applications that allow altering digital images and their ease of use, they weaken the credibility of an image. This problem, together with the ease of distributing information through the Internet (blogs, social networks, etc.), has led to a tendency for information to be accepted as true without its veracity being questioned. Image counterfeiting has become a major threat to the credibility of the information. To deal with this threat, forensic image analysis is aimed at detecting and locating image forgeries using multiple clues that allows it to determine the veracity or otherwise of an image. In this paper, we present a method for the authentication of images. The proposed method performs detection of copy-move alterations within an image, using the discrete cosine transform. The characteristics obtained from these coefficients allow us to obtain transfer vectors, which are grouped together. Through the use of a tolerance threshold, it is possible to determine whether there are regions copied and pasted within the analysed image. The results obtained from the experiments reported in this paper demonstrate the effectiveness of the proposed method. For the evaluation of the proposed methods, experiments were carried out with public databases of falsified images that are widely used in the literature.

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References

  1. Alahmadi A, Hussain M (2017) Passive detection of image forgery using DCT and local binary pattern. Signal Image Video Process 11(1):81–88. https://doi.org/10.1007/s11760-016-0899-0

    Article  Google Scholar 

  2. Alkawaz MH, Sulong G, Saba T, Rehman A (2018) Detection of copy-move image forgery based on discrete cosine transform. Neural Comput Appl 30(1):183–192

    Article  Google Scholar 

  3. 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 

  4. Ardizzone E, Bruno A, Mazzola G (2015) Copy-move forgery detection by matching triangles of keypoints. IEEE Trans Inf Forensics Secur 10(10):2084–2094

    Article  Google Scholar 

  5. Bharati A, Singh R, Vatsa M, Bowyer KW (2016) Detecting facial retouching using supervised deep learning. IEEE Trans Inf Forensics Secur 11(9):1903–1913. https://doi.org/10.1109/TIFS.2016.2561898

    Article  Google Scholar 

  6. Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10(3):226–245

    Article  Google Scholar 

  7. Bo X, Junwen W, Guangjie L, Yuewei D (2010) Image copy-move forgery detection based on SURF. In: 2010 international conference on multimedia information networking and security. Nanjing, China, pp 889–892. https://doi.org/10.1109/MINES.2010.189

  8. Chen M, Fridrich J, Goljan M, Lukas J (2008) Determining image origin and integrity using sensor noise. IEEE Trans Inf Forensics Secur 3(1):74–90. https://doi.org/10.1109/TIFS.2007.916285

    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

    Article  Google Scholar 

  10. Columbia University: Columbia DVMM image splicing datasets (2011). http://www.ee.columbia.edu/ln/dvmm/newDownloads.htm

  11. 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 

  12. Ding F, Zhu G, Dong W, Shi YQ (2018) An efficient weak sharpening detection method for image forensics. J Vis Commun Image Represent 50:93–99

    Article  Google Scholar 

  13. Ding F, Zhu G, Yang J, Xie J, Shi Y (2015) Edge perpendicular binary coding for USM sharpening detection. IEEE Signal Process Lett 22(3):327–331. https://doi.org/10.1109/LSP.2014.2359033

    Article  Google Scholar 

  14. Dong J, Wang, W (2019) CASIA TIDE v1.0 - v2.0. http://forensics.idealtest.org/

  15. Freeman L (2017) Digital evidence and war crimes prosecutions: the impact of digital technologies on international criminal investigations and trials. Fordham Int Law J 41(2):283

    Google Scholar 

  16. Fridrich J, Soukal D, Lukas J (2003) Detection of copy move forgery in digital images. In: Proceedings of the digital forensic research workshop. Binghamton, New York, pp 5–8

  17. Fu Q, Zhou X, Wang C, Jiang B (2016) Mathematical relation between APBT-based and DCT-based JPEG image compression schemes. J Commun 11:84–92

    Google Scholar 

  18. Huang F, Huang J, Shi YQ (2010) Detecting double JPEG compression with the same quantization matrix. IEEE Trans Inf Forensics Secur 5(4):848–856. https://doi.org/10.1109/TIFS.2010.2072921

    Article  Google Scholar 

  19. Huang H, Guo W, Zhang Y (2008) Detection of copy-move forgery in digital images using SIFT algorithm. In: 2008 IEEE Pacific-Asia workshop on computational intelligence and industrial application, vol 2, pp 272–276. https://doi.org/10.1109/PACIIA.2008.240

  20. Kang X, Wei S (2008) Identifying tampered regions using singular value decomposition in digital image forensics. In: 2008 international conference on computer science and software engineering, vol 3, pp 926–930. https://doi.org/10.1109/CSSE.2008.876

  21. Kee E, Farid H (2011) A perceptual metric for photo retouching. Natl Acad Sci 108(50):19907–19912. https://doi.org/10.1073/pnas.1110747108

    Article  Google Scholar 

  22. Listverse: Top 15 photoshopped photos that fooled us all (2007) http://listverse.com/2007/10/19/top-15-manipulated-photographs/

  23. Mahdian B, Saic S (2010) A bibliography on blind methods for identifying image forgery. Signal Process Image Commun 25(6):389–399

    Article  Google Scholar 

  24. Malin DF (1977) Unsharp masking. Am Astron Soc Photo Bull 16:10–13

    Google Scholar 

  25. Niu Y, Li X, Zhao Y, Ni R (2019) An enhanced approach for detecting double JPEG compression with the same quantization matrix. Signal Process Image Commun 76:89–96

    Article  Google Scholar 

  26. Park CS, Choeh JY (2018) Fast and robust copy-move forgery detection based on scale-space representation. Multimed Tools Appl 77(13):16795–16811

    Article  Google Scholar 

  27. Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. Department of Computer Science, vol 646

  28. Shi YQ, Chen C, Chen W (2007) A natural image model approach to splicing detection. In: Proceedings of the 9th workshop on multimedia and security. Dallas, Texas, pp 51–62. https://doi.org/10.1145/1288869.1288878

  29. 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 

  30. da Silva EAB, Mendonça GV (2005) 4—digital image processing. In: Chen WK (ed) The electrical engineering handbook. Academic Press, Burlington, pp 891–910

    Chapter  Google Scholar 

  31. Teerakanok S, Uehara T (2019) Copy-move forgery detection: a state-of-the-art technical review and analysis. IEEE Access 7:40550–40568. https://doi.org/10.1109/ACCESS.2019.2907316

    Article  Google Scholar 

  32. Tralic D, Zupancic I, Grgic S, Grgic M (2013) Comofod–new database for copy-move forgery detection. In: Proceedings ELMAR-2013. IEEE, pp 49–54

  33. Wang W, Dong J, Tan T (2010) Image tampering detection based on stationary distribution of Markov chain. In: 2010 IEEE international conference on image processing. Hong Kong, China, pp. 2101–2104. https://doi.org/10.1109/ICIP.2010.5652660

  34. Xia Z, Yuan C, Sun X, Sun D, Lv R (2016) Combining wavelet transform and LBP related features for fingerprint liveness detection. IAENG Int J Comput Sci 43(3):290–298

    Google Scholar 

  35. Zhang Z, Kang J, Ren Y (2008) An effective algorithm of image splicing detection. In: 2008 international conference on computer science and software engineering, vol 1, pp 1035–1039. https://doi.org/10.1109/CSSE.2008.1621

  36. Zhang Z, Wang D, Wang C, Zhou X (2017) Detecting copy-move forgeries in images based on DCT and main transfer vectors. KSII Trans Internet Inf Syst 11:4567–4587

    Google Scholar 

  37. Zhao J, Guo J (2013) Passive forensics for copy-move image forgery using a method based on DCT and SVD. Forensic Sci Int 233(1):158–166

    Article  Google Scholar 

  38. Zhao X, Li J (2011) Detecting digital image splicing in chroma spaces. In: Digital watermarking. Springer, Berlin, vol 6526, pp 12–22

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 700326. Website: http://ramses2020.eu

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Authors and Affiliations

  1. Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040, Madrid, Spain

    Esteban Alejandro Armas Vega, Edgar González Fernández, Ana Lucila Sandoval Orozco & Luis Javier García Villalba

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  1. Esteban Alejandro Armas Vega
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  2. Edgar González Fernández
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  3. Ana Lucila Sandoval Orozco
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  4. Luis Javier García Villalba
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Correspondence to Luis Javier García Villalba.

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Armas Vega, E.A., González Fernández, E., Sandoval Orozco, A.L. et al. Copy-move forgery detection technique based on discrete cosine transform blocks features. Neural Comput & Applic 33, 4713–4727 (2021). https://doi.org/10.1007/s00521-020-05433-1

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