Abstract
Copy–move forgery is a well-known image forgery technique. In this image manipulation method, a certain area of the image is replicated and affixed over the same image on different locations. Most of the times replicated segments suffer from multiple post-processing and geometrical attacks to hide sign of tampering. We have used block-based method for forgery detection. In block-based proficiencies, image is parted into partially overlapping blocks. Features are extracted corresponding to blocks. In the proposed scheme, we have computed Gray-Level Co-occurrence Matrix (GLCM) for blocks. Singular Value Decomposition (SVD) is applied over GLCM to find singular values. We have calculated Local Binary Pattern (LBP) for all blocks. The singular values and LBP features combinedly construct feature vector corresponding to blocks. These feature vectors are sorted lexicographically. Further, similar blocks discovered to identify replicated section of image. To ensure endurance of the proposed methods, Detection Accuracy (DA), False Positive Rate (FPR), and F-Measure are calculated and compared with existing methods. Experimental results establish the validity of proposed scheme for precise detection, even when meddled region of image sustain distortion due to brightness change, blurring, color reduction, and contrast adjustment.
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Zandi, M., Mahmoudi-Aznaveh, A., Talebpour, A.: Iterative copy-move forgery detection based on a new interest point detector. IEEE Trans. Inf. Forensics Secur. 11(11), 2499–2512 (2016). https://doi.org/10.1109/TIFS.2016.2585118
Chen, C., Ni, J., Shen, Z., Shi, Y.Q.: Blind forensics of successive geometric transformations in digital images using spectral method: theory and applications. IEEE Trans. Image Process. 26(6), 2811–2824 (2017). https://doi.org/10.1109/TIP.2017.2682963
Fridrich, J., Soukal, D., Lukas, J.: Detection of copy-move forgery in digital images. In: Digital Forensic Research Workshop, pp. 55–61. IEEE Computer Society (2003)
Alkawaz, M.H., Sulong, G., Saba, T., Rehman, A.: Detection of copy-move image forgery based on discrete cosine transform. Neural Comput. Appl. 1–10 (2016). https://doi.org/10.1007/s00521-016-2663-3
Zandi, M., Mahmoudi-Aznaveh, A., Mansouri, A.: Adaptive matching for copy-move forgery detection. IEEE International Workshop on Information Forensics and Security (WIFS), pp. 119–124 (2014). https://doi.org/10.13140/RG.2.1.2189.5200
Lee, J.C., Chang, C.P., Chen, W.K.: Detection of copy-move image forgery using histogram of orientated gradients. Inf. Sci. 321(C), 250–262 (2015). https://doi.org/10.1016/j.ins.2015.03.009
Silva, E., Carvalho, T., Ferreira, A., Rocha, A.: 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 (2015). https://doi.org/10.1016/j.jvcir.2015.01.016
Shen, X., Shi, Z., Chen, H.: Splicing image forgery detection using textural features based on grey level co-occurrence matrices. IET Image Process. 11(1), 44–53 (2017). https://doi.org/10.1049/iet-ipr.2016.0238
Tai, Y., Yang, J., Luo, L., Zhang, F., Qian, J.: Learning discriminative singular value decomposition representation for face recognition. Pattern Recognit. 50, 1–16 (2016). https://doi.org/10.1016/j.patcog.2015.08.010
Zhang, T., Wang, R.: Copy-move forgery detection based on SVD in digital images. In: International Congress on Image and Signal Processing, pp. 1–5 (2009). https://doi.org/10.1109/CISP.2009.5301325
Li, L., Li, S., Zhu, H., Chu, S.C., Roddick, J.F., Pan, J.S.: An efficient scheme for detecting copy-move forged images by local binary patterns. J. Inf. Hiding Multimed. Signal Process. 4(1), 46–56 (2013)
Li, Z., Liu, G.Z., Yang, Y., You, Z.Y.: Scale and rotation-invariant local binary pattern using scale-adaptive texton subuniform-based circular shift and sub uniform-based circular shift. IEEE Trans. Image Process. 21(4), 2130–2140 (2012). https://doi.org/10.1109/TIP.2011.2173697
Tralic, D., Zupancic, I., Grgic, S., Grgic, M.: CoMoFoD—new database for copy-move forgery detection. In: International Symposium Electronics in Marine, pp. 49–54 (2013)
Khan, S., Kulkarni, A.: An efficient method for detection of copy-move forgery using discrete wavelet transform. Int. J. Comput. Sci. Eng. 2(5), 1801–1806 (2010)
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Dixit, A., Bag, S. (2020). Copy–Move Image Forgery Detection Using Gray-Tones with Texture Description. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-32-9291-8_7
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DOI: https://doi.org/10.1007/978-981-32-9291-8_7
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