Abstract
Understanding is considered a key purpose of image forensic science in order to find out if a digital image is authenticated or not. It can be a sensitive task in case images are used as necessary proof as an impact judgment. it’s known that There are several different manipulating attacks but, this copy move is considered as one of the most common and immediate one, in which a region is copied twice in order to give different information about the same scene, which can be considered as an issue of information integrity. The detection of this kind of manipulating has been recently handled using methods based on SIFT. SIFT characteristics are represented in the detection of image features and determining matched points. A clustering is a key step which always following SIFT matching in-order to classify similar matched points to clusters. The ability of the image forensic tool is represented in the assessment of the conversion that is applied between the two duplicated images of one region and located them correctly. Detecting copy-move forgery is not a new approach but using a new clustering approach which has been purposed by using the 2-level clustering strategy based on spatial and transformation domains and any previous information about the investigated image or the number of clusters need to be created is not necessary. Results from different data have been set, proving that the proposed method is able to individuate the altered areas, with high reliability and dealing with multiple cloning.






Similar content being viewed by others
References
Amerini I, Ballan L, Caldelli R, DelBimbo A, Serra G (2011) ASIFT based forensic method for copy move attack detection and transformation recovery. IEEE Trans Inf Forensics Secur 6(3):1099–1110
Amerini I, Ballan L, Caldelli R, DelBimbo A, Serra G (2014) Copy-move forgery detection and localization by means of robust clustering with J-Linkage
Barni M, Bartolini F (2004) “Watermarking systems engineering enabling” Digital assets security and other applications. Marcel Dekker
Bashar M, Noda K, Ohnishi N, Mori K (2018) Exploring duplicated regions in natural images. IEEE Trans Image Process
Bayram S, Avcibas I, Sankur B, Memon N (2005) Image manipulation detection with binary similarity measures. In: Proc. of EUSIPCO, Antalya, Turkey
Bayram S, Sencar HT, Memon N (2008) A survey of copy-move forgery detection techniques. In: Proc. of IEEE Western New York Image Processing Workshop
Bayram S, TahaSencar H, Memon N (2009) An efficient and robust method for detecting copy-move forgery. In: Proc. of IEEE ICASSP, Washington, DC, USA
Bravo Solorio S, Nandi AK (2009) Passive method for detecting duplicated regions affected by reflection, rotation and scaling. In: Proc. of EUSIPCO, Glasgow, Scotland
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
Christlein V, Riess C, Angelopoulou E (2010) On rotation invariance in copy-move forgery detection. In: Proc. of IEEE WIFS, Seattle, WA, USA
Cox IJ, Miller ML, Bloom JA (2002) Digital watermarking. Morgan Kaufmann, San Francisco
Fahim A, Saake G, Salem A, Torkey F, Ramadan M (2009) Improved DBSCAN for spatial databases with noise and different densities. GESJ 3(20):53–60
Farid H (2009) Photo fakery and forensics. Adv Comput 77:1–55
Farid H (2009) A survey of image forgery detection. IEEE Signal Process Mag 2(26):16–25
Farid H, Lyu S (2003) Higher-order wavelet statistics and their application to digital forensics. In: Proc. of IEEE CVPR Workshop on Statistical Analysis in Computer Vision, Madison, WI, USA
Fischlerand MA, Bolles RC Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395
Fridrich J, Soukal D, Lukás J (2003) Detection of copy-move forgery in digital images. In: Proc. of DFRWS
Fridrich J, Soukal D , Lukás J (2003) Detection of copy-move forgery in digital images. In: Proc. of DFRWS, Cleveland, OH, USA
Fridrich J, Soukal D, Luḱas J (2003) Detection of copy-move forgery in digital images. In: Proc. of DFRWS
He Z, Sun W, Lu W, Lu H (2011) Digital image splicing detection based on approximate run length. Pattern Recogn Lett 32(12):1591–1597
He Z, Lu W, Sun W, Huang J (2012) Digital image splicing detection based on markov features in DCT and DWT domain. Pattern Recogn 45(12):4292–4299
Huang Y, Lu W, Sun W, Long D (2011) Improved DCT based detection of copy-move forgery in images. Forensic Sci Int 206(1–3):178–184
Kakar P, Sudha N (2012) Exposing post-processed copy-paste forgeries through transform invariant features. IEEE Trans Inf Forensics Secur 7(3):1018–1028
Kaur H, Saxena J, Singh S (2015) Simulative Comparison of Copy- Move Forgery Detection Methods for Digital Images. International Journal of Electronics, Electrical and Computational System IJEECS, ISSN 2348-117X Volume 4, Special Issue
Li G, Wu Q, Tu D, Sun SJ (2007) A sorted neighborhood approach for detecting duplicated region in image forgeries based on DWT and SVD. In: Proc. of IEEE ICME, Beijing, China
Lin Z, He J, Tang X, Tang CK (2009) Fast automatic and fine grained tampered JPEG image detection via DCT conceit analysis. Pattern Recogn 42(11):2492–2501
Lin HJ, Wang CW, Kao YT (2009) Fast copy-move forgery detection. WSEAS Trans Sig Proc 5(5):188–197
Lowe DG (2004) Distinctive image features from scale invariant keypoints. Int J Comput Vis 60(2):91–110
Lyu S, Farid H (2005) How realistic is photorealistic? IEEE Trans Signal Process 53(2):845–850
Mohamed Mursi MF, Salama MM, Habeb MH (2017) An improved SIFT-PCA-based copy-move image forgery detection method. International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) 6(3)
Ng T-T, Chang S-F, Hsu J, Pepeljugoski M (2004) Columbia photo-graphic images and photorealistic computer graphics dataset. ADVENT, Columbia University, Tech. Rep
Popescu A, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. Dartmouth College, Computer Science Tech. Rep. TR2004–515
Popescu A, Farid H (2005) Statistical tools for digital forensics. In: Proc. of Int. Workshop on Information Hiding, Toronto, Canada
Popescu AC, Farid H (2005) Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process 53(2):758–767
Redi JA, Taktak W, Dugelay JL (2011) Digital image for ensics: a booklet for beginners. Multimed Tools Appl 51(1):133–162
Ryu SJ, Lee MJ, Lee HK (2010) Detection of copy – rotate – move forgery using zernike moments. In: Proc. of International Workshop on Information Hiding, Calgary, Canada
Singh RD, Aggarwal N (2017) Detection and localization of copy-paste forgeries in digital videos. Forensic Sci Int 281:75–91
Swaminathan A, Wu M, Liu K (2008) Digital image forensics via intrinsic fingerprints. IEEE Trans Inf Forensics Secur 3(1):101–117
Wang Z, Bovik AC, Sheikhand HR, Simoncelli EP (2004) Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Wang J, Liu G, Li H, Dai Y, Wang Z (2009) Detection of image region duplication forgery using model with circle block. In: Proc. of MINES, Washington, DC, USA
Yanga F, Lia J, Lua W, Wengb J (2017) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intell 59:73–83
Acknowledgements
The authors would like to thank the anonymous reviewers for their helpful and constructive comments that greatly contributed to improving the final version of the paper. They would also like to thank the Editors for their generous comments and support during the review process. Finally, they would like to thank Dr. Hana Hamza for her constructive suspensions and propositions that have helped a lot to improve research quality.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Abdel-Basset, M., Manogaran, G., Fakhry, A.E. et al. 2-Levels of clustering strategy to detect and locate copy-move forgery in digital images. Multimed Tools Appl 79, 5419–5437 (2020). https://doi.org/10.1007/s11042-018-6266-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-018-6266-0