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Cloning detection scheme based on linear and curvature scale space with new false positive removal filters

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Abstract

Recently, tampering in digital images considered the main challenge in image forensic analyses. Hence, copying a part and pasting it in the same image became the most crucial action in image forgery. It threatens the integrity and authenticity of image ownership. The intruder utilizes the development tools of image processing programs to make the forged image the same as the authentic one and strict for detection. This work as copy-move forgery detection (CMFD) manipulates the problem of a few key points in small-size and homogenous digital images by adopting a merging scheme to detects sufficient key points by using a linear scale-space detector based on Speedup Robust Feature(SURF) and curvature scale space detector based on Maximally Stable Extremal Region (MSER). Afterward, these key points are described distinctively by extract unique vectors, and matching these vectors to find duplicated regions. In the post-processing stage, we propose new filters, the first is called Parallel Filter and the other called Distance Ratio Filter. These Filters aim to remove false-positive results and boost true positive results thus improving the accuracy of the detection scheme. The experimental results on standard data sets (MICC 220, F8 Multi) show that CMFD is efficient and insensitive against simple and combination post-processing attacks like photometric and geometric transformations. Also, it is invariant against non-uniform transformation like (skew, wrap), and detects multi cloning efficiently with a high true-positive ratio (TPR=98.5) and low false-positive ratio (FPR=4).

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Data availability

The datasets generated and/or analyzed during the current study are not publicly available due [REASON WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request.

References

  1. Alberry HA, Hegazy AA, Salama GI (2018) A fast SIFT-based method for copy-move forgery detection. Future Comput Inform J 3:159–165

    Article  Google Scholar 

  2. Al-Hammadi MM, Emmanuel S (2016) Improving SURF based copy-move forgery detection using super resolution. 2016 IEEE International Symposium on Multimedia (ISM). IEEE

  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 6:1099–1110

    Article  Google Scholar 

  4. Asghar K, Habib Z, Hussain M (2016)Copy-move and splicing image forgery detection and localization techniques: a review. 0618:2–4. https://doi.org/10.1080/00450618.2016.1153711

  5. Bakiah N, Warif A, Wahid A, Wahab A, Yamani M, Idris I, Ramli R, Salleh R, Shamshirband S (2016)Copy-move forgery detection: Survey, challenges and future directions. J Netw Comput Appl 75:260–263. https://doi.org/10.1016/j.jnca.2016.09.008

    Article  Google Scholar 

  6. Dixit A, Gupta RK (2016)Copy-move image forgery detection a review. Int J Image Graph Signal Process 8:29–40. https://doi.org/10.5815/ijigsp.2016.06.04

    Article  Google Scholar 

  7. Fadl SM, Semary NA (2017) Robust copy-move forgery revealing in digital images using polar coordinate system. Neurocomputing. https://doi.org/10.1016/j.neucom.2016.11.091

  8. Fan XH, Lin WT, Cao J, Li BL, Si Y (2012) A description method for MSER with SIFT descriptor. Appl Mech Mater 127:115–120

  9. Fridrich AJ, Soukal BD, Lukáš AJ (2003) Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop. Citeseer

  10. Hassaballah M, Abdelmgeid AA, Alshazly HA (2016) Image features detection, description and matching. Image Feature Detectors and Descriptors. Springer, Berlin

  11. Kaspi O, Yosipof A, Senderowitz H (2017) RANdom SAmple Consensus (RANSAC) algorithm for material–informatics: application to photovoltaic solar cells. J Cheminform. https://doi.org/10.1186/s13321-017-0224-0

    Article  Google Scholar 

  12. Kucherov AS, Krezhov KA, Tansel IN, Singh G, Korla S, Grisso BL, Salvino LW, Uragun B, Vasily A, Gregory P (2011) Rast 2011- Proceedings of 5th International Conference on Recent Advances in Space Technologies. Optimization 365:369

  13. Li Y, Member S, Zhou J (2018) Fast and effective image copy-move forgery detection via hierarchical feature point matching. IEEE Trans Inf Forensics Secur: 1. https://doi.org/10.1109/TIFS.2018.2876837

  14. Liu X-, Wang P (2016) Robust copy – move forgery detection using quaternion exponent moments. Pattern Anal Appl: 1–2. https://doi.org/10.1007/s10044-016-0588-1

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

    Article  Google Scholar 

  16. Mahmood T, Mehmood Z, Shah M, Saba T (2018) A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform. J Vis Commun Image Represent 53:202–214

    Article  Google Scholar 

  17. Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, Van Gool L (2005) A comparison of affine region detectors. Int J Comput Vision 65:43–72

    Article  Google Scholar 

  18. Muzaffer G, Uluta G, Gedikli E (2017) PSO and SURF based digital image forgery detection.2017 International Conference on Computer Science and Engineering  (UBMK). IEEE

  19. Niyishaka P, Bhagvati C (2020)Copy-move forgery detection using image blobs and BRISK feature. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-09225-6

    Article  Google Scholar 

  20. Sadeghi S, Dadkhah S, Jalab HA, Mazzola G, Uliyan D (2017) State of the art in passive digital image forgery detection: copy–move image forgery. Pattern Anal Appl. https://doi.org/10.1007/s10044-017-0678-8

    Article  Google Scholar 

  21. Salahat E, Qasaimeh M (2017) Recent advances in features extraction and description algorithms: A comprehensive survey. 2017 IEEE international conference on industrial technology (ICIT). IEEE

  22. 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 

  23. Vaishnavi D, Subashini TS (2019) Application of local invariant symmetry features to detect and localize image copy move forgeries. J Inf Secur Appl 44:23–31. https://doi.org/10.1016/j.jisa.2018.11.001

    Article  Google Scholar 

  24. Walia S, Kumar K (2018) Digital image forgery detection: a systematic scrutiny. Aust J Forensic Sci 0618:1–39. https://doi.org/10.1080/00450618.2018.1424241

    Article  Google Scholar 

  25. Wang C, Zhang Z (2018) SS symmetry an image copy-move forgery detection scheme based on A-KAZE and SURF features. 1-20. https://doi.org/10.3390/sym10120706

  26. Warif NBA, Wahab AWA, Idris MYI, Salleh R, Othman F (2017) SIFT-symmetry: a robust detection method for copy-move forgery with reflection attack. J Vis Commun Image Represent 46:219–232

    Article  Google Scholar 

  27. Yang B, Sun X, Guo H (2017) A copy-move forgery detection method based on CMFD-SIFT. Multimed Tools Appl. https://doi.org/10.1007/s11042-016-4289-y

    Article  Google Scholar 

  28. Yang H-Y, Qi S-R, Niu Y, Niu P-P, Wang X-Y(2019)Copy-move forgery detection based on adaptive keypoints extraction and matching. Multimed Tools Appl 78:34585–34612

    Article  Google Scholar 

  29. Zhang G, Wang H (2012) SURF-based detection of copy-move forgery in flat region. Int J Adv Comput Technol 4:521–529

  30. Zhang W, Yang Z, Niu S (2017) Detection of copy-move forgery in flat region based on feature enhancement. 2:159–171. https://doi.org/10.1007/978-3-319-53465-7

  31. Zhang Z, Wang C, Zhou X (2018) A survey on passive image copy-move forgery detection. J Inf Process Syst 14(1):6–31. https://doi.org/10.3745/JIPS.02.0078

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Manaf Mohammed Ali Alhaidery, Amir Hossein Taherinia & Hadi Sadoghi Yazdi

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  1. Manaf Mohammed Ali Alhaidery
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  2. Amir Hossein Taherinia
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  3. Hadi Sadoghi Yazdi
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Correspondence to Amir Hossein Taherinia.

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Alhaidery, M.M.A., Taherinia, A.H. & Yazdi, H.S. Cloning detection scheme based on linear and curvature scale space with new false positive removal filters. Multimed Tools Appl 81, 8745–8766 (2022). https://doi.org/10.1007/s11042-022-12237-z

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