Abstract
Copy-move image forgery is a type of digital image forgery where content is copied and pasted within the same image, either by hiding foreground objects within an image by copying a background region over them or by emphasizing some foreground objects of the image through duplication. To prevent any consequences arising from the consumption of these forged images, many Copy-Move Forgery Detection (CMFD) techniques have been proposed in the past that analyze a suspected input image for a possible copy-move forgery. However, the existing detectors show limited detection accuracy in presence of post-processing manipulations like noise addition, compression, blur etc., which, in turn, are often used to hide the traces of tampering and produce convincing forgeries. In this paper, we propose a block-based CMFD technique which works well under these post-processing manipulations. We use Discrete Cosine Transform and Cellular Automata to extract features from the image blocks, which are subsequently matched using the patch match algorithm. Also, to extract the cloned regions corresponding to the matched features in a reliable way, we propose a simple and efficient CA-based post-processing procedure. The experimental results on the standard dataset demonstrate the effectiveness of our method for detecting copy-move forgeries under diverse post-processing manipulations of noise, compression, blur, brightness change, contrast change and their combinations.
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References
Agarwal R, Verma OP (2020) An efficient copy move forgery detection using deep learning feature extraction and matching algorithm. Multimed Tools Appl 79(11–12):7355–7376. https://doi.org/10.1007/s11042-019-08495-z
Al-Qershi OM, Khoo BE (2018) Evaluation of copy-move forgery detection: datasets and evaluation metrics. Multimed Tools Appl 77(24):31807–31833. https://doi.org/10.1007/s11042-018-6201-4
Al-Qershi OM, Khoo BE (2019) Enhanced block-based copy-move forgery detection using k-means clustering. Multidimen Syst Signal Process. https://doi.org/10.1007/s11045-018-0624-y
Amerini I, Ballan L, Caldelli R, Del Bimbo AD, 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. https://doi.org/10.1109/TIFS.2011.2129512
Ardizzone E, Bruno A., Mazzola G (2015) Copy-move forgery detection by matching triangles of keypoints. IEEE Trans Inform Forens Secur. https://doi.org/10.1109/TIFS.2015.2445742
Bakiah N, Warif A, Yamani M, Idris I, Wahid A, Wahab A, Salleh R, Ismail A (2018) CMF-iteMS:An automatic threshold selection for detection of copy-move forgery. Forens Sci Int. https://doi.org/10.1016/j.forsciint.2018.12.004
Barnes C, Shechtman E, Finkelstein A, Goldman DB (2009) PatchMatch: A randomized correspondence algorithm for structural image editing. ACM Trans Graph 28(3). https://doi.org/10.1145/1531326.1531330
Billings SA, Yang Y (2003) Identification of the neighborhood and CA rules from spatio-temporal CA patterns. In: IEEE transactions on systems, man and cybernetics Part B cybernetics. https://doi.org/10.1109/TSMCB.2003.810438
Cao Y, Gao T, Fan L, Yang Q (2012) A robust detection algorithm for copy-move forgery in digital images. Forensic Sci Int 214(1–3):33–43. https://doi.org/10.1016/j.forsciint.2011.07.015
Chen CC, Lu WY, Chou CH (2019) Rotational copy-move forgery detection using SIFT and region growing strategies. Multimed Tools Appl. https://doi.org/10.1007/s11042-019-7165-8
Chen B, Tan W, Coatrieux G, Zheng Y, Shi YQ (2020) A serial image copy-move forgery localization scheme with source/target distinguishment. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2020.3026868
Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inform Forens Secur 7(6):1841–1854. https://doi.org/10.1109/TIFS.2012.2218597
Cozzolino D, Poggi G, Verdoliva L (2015a) Efficient dense-field copy-move forgery detection. IEEE Trans Inform Forens Secur 10(11):2284–2297. https://doi.org/10.1109/TIFS.2015.2455334
Davarzani R, Yaghmaie K, Mozaffari S, Tapak M (2013) Copy-move forgery detection using multiresolution local binary patterns. Forensic Sci Int 231 (1–3):61–72. https://doi.org/10.1016/j.forsciint.2013.04.023
Elaskily MA, Elnemr HA, Sedik A, Dessouky MM, El Banby GM, Elshakankiry OA, Khalaf AAM, Aslan HK, Faragallah OS, Abd El-Samie FE (2020) A novel deep learning framework for copy-moveforgery detection in images. Multimed Tools Appl 79:19167–19192. https://doi.org/10.1007/s11042-020-08751-7
Fridrich J, Soukal D., Lukáš J (2003) Detection of copy-move forgery in digital images. Int J Comput Sci Issues. https://doi.org/10.1109/PACIIA.2008.240
Gani G, Qadir F (2019) A novel method for digital image copy – move forgery detection and localization using evolving cellular automata and local binary patterns. Evol Syst 0123456789. https://doi.org/10.1007/s12530-019-09309-1
Hayat K, Qazi T (2017) Forgery detection in digital images via discrete wavelet and discrete cosine transforms. Comput Electr Eng 0:1–11. https://doi.org/10.1016/j.compeleceng.2017.03.013
Hong C, Yu J, Zhang J, Jin X, Lee KH (2019) Multimodal face-pose estimation with multitask manifold deep learning. IEEE Trans Indust Inform. https://doi.org/10.1109/TII.2018.2884211
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. https://doi.org/10.1016/j.forsciint.2010.08.001
Jeelani Z, Qadir F (2018) Cellular automata-based approach for digital image scrambling. Int J Intell Comput Cybern 11(3):353–370. https://doi.org/10.1108/IJICC-10-2017-0132
Jeelani Z, Qadir F (2019) Cellular automata-based approach for salt-and-pepper noise filtration. J King Saud Univ Comput Inform Sci. https://doi.org/10.1016/j.jksuci.2018.12.006
Kaura WCN, Dhavale S (2018) Analysis of SIFT and SURF features for copy-move image forgery detection. In: Proceedings of 2017 international conference on innovations in information, embedded and communication systems ICIIECS. https://doi.org/10.1109/ICIIECS.2017.8276160, p 2017
Li L, Li S, Zhu H, Chu S-C, Roddick JF, Pan J-S (2013) An efficient scheme for detecting copy-move forged images by local binary patterns. J Inform Hid Multimed Signal Process
Li C, Guo C, Ren W, Cong R, Hou J, Kwong S, Tao D (2020) An underwater image enhancement benchmark dataset and beyond. IEEE Trans Image Process 29(c):4376–4389. https://doi.org/10.1109/TIP.2019.2955241
Lin C, Lu W, Huang X, Liu K, Sun W, Lin H., Tan Z (2019) Copy-move forgery detection using combined features and transitive matching. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6922-4
Liu Y, Wang H, Chen Y, Wu H., Wang H (2020) A passive forensic scheme for copy-move forgery based on superpixel segmentation and K-means clustering. Multimed Tools Appl. https://doi.org/10.1007/s11042-019-08044-8
Mahmood T, Irtaza A, Mehmood Z, Tariq Mahmood M (2017) Copy–move forgery detection through stationary wavelets and local binary pattern variance for forensic analysis in digital images. Forensic Sci Int. https://doi.org/10.1016/j.forsciint.2017.07.037
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. https://doi.org/10.1016/j.jvcir.2018.03.015
Nightingale SJ, Wade KA, Watson DG (2017) Can people identify original and manipulated photos of real-world scenes? Cognit Res Princip Impli 2(1). https://doi.org/10.1186/s41235-017-0067-2
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66. https://doi.org/10.1109/TSMC.1979.4310076
Rosin PL (2010) Image processing using 3-state cellular automata. Computer Vision and Image Understanding. https://doi.org/10.1016/j.cviu.2010.02.005
Roy A, Konda A, Chakraborty RS (2018) Copy move forgery detection with similar but genuine objects. In: Proceedings - international conference on image processing, ICIP. https://doi.org/10.1109/ICIP.2017.8297050
Roy A, Dixit R, Naskar R, Chakraborty RS (2020) Copy-move forgery detection with similar but genuine objects. In: Studies in computational intelligence. https://doi.org/10.1007/978-981-10-7644-2_5
Roy S, Shrivastava M, Pandey CV, Nayak SK, Rawat U (2020) IEVCA: An efficient image encryption technique for IoT applications using 2-D Von-Neumann cellular automata. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-09880-9
Soni B, Das PK, Meitei D (2019) Geometric transformation invariant block based copy-move forgery detection using fast and efficient hybrid local features. J Inform Secur Appl 45:44–51. https://doi.org/10.1016/j.jisa.2019.01.007
Sun X, Rosin PL, Martin RR (2011) Fast rule identification and neighborhood selection for cellular automata. In: IEEE Transactions on Systems, Man and Cybernetics Part B Cybernetics. https://doi.org/10.1109/TSMCB.2010.2091271
Teerakanok S, Uehara T (2019) Copy-move forgery detection: A state-of-the-art technical review and analysis. IEEE Access 7(c):40550–40568. https://doi.org/10.1109/ACCESS.2019.2907316
Tralic D, Zupancic I, Grgic SGM (2013) New Database for Copy-Move Forgery Detection-CoMoFoD. 55th International Symposium ELMAR, 49–54. http://www.vcl.fer.hr/comofod/
Tralic D, Grgic S, Sun X, Rosin PL (2016) Combining cellular automata and local binary patterns for copy-move forgery detection. Multimed Tools Appl 75(24):16881–16903. https://doi.org/10.1007/s11042-015-2961-2
Wang H, Wang H (2018) Perceptual hashing-based image copy-move forgery detection. Secur Commun Netw 2018:11. Article ID 6853696 . https://doi.org/10.1155/2018/6853696
Wang C, Zhang Z, Li Q, Zhou X (2019) An image copy-move forgery detection method based on SURF and PCET. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2955308
Wolfram S (2002) A new kind of science. In Wolfram Media. Wolfram Media. www.wolframscience.com
Wu Y, Abd-Almageed W, Natarajan P (2018) BusterNet: Detecting copy-move image forgery with source/target localization. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer vision – ECCV 2018. ECCV 2018. Lecture notes in computer science. 11210 LNCS, 170–186. https://doi.org/10.1007/978-3-030-01231-1_11, vol 11210. Springer, Cham
Yang B, Sun X, Guo H, Xia Z., Chen X (2018) A copy-move forgery detection method based on CMFD-SIFT. Multimed Tools Appl. https://doi.org/10.1007/s11042-016-4289-y
Yu J, Tan M, Zhang H, Tao D, Rui Y (2019) Hierarchical Deep Click Feature Prediction for Fine-grained Image Recognition. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/tpami.2019.2932058
Zhao J, Guo J (2013) Passive forensics for copy-move image forgery using a method based on DCT and SVD. Forensic Sci Int 233(1–3):158–166. https://doi.org/10.1016/j.forsciint.2013.09.013
Zhong J-L, Pun C-M (2019) An end-to-end dense-inceptionnet for image copy-move forgery detection. IEEE Trans Inform Forens Secur 15(c):2134–2146. https://doi.org/10.1109/tifs.2019.2957693
Zhou P, Han X, Morariu VI, Davis LS (2018) Learning rich features for image manipulation detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. https://doi.org/10.1109/CVPR.2018.00116
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Gani, G., Qadir, F. Copy move forgery detection using DCT, PatchMatch and cellular automata. Multimed Tools Appl 80, 32219–32243 (2021). https://doi.org/10.1007/s11042-021-11174-7
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DOI: https://doi.org/10.1007/s11042-021-11174-7