Image Splicing Detection Scheme Using Surf and Mean-LBP Based Morphological Operations
DOI:
https://doi.org/10.25271/sjuoz.2021.9.4.866Keywords:
Splicing Forgery detection, Texture features, Support Vector Machine, SURF, MEAN-LBPAbstract
Tampering with images and changing them without giving any evidence has become very popular because of the presence of an enormous degree of intense altering device. The image forensics technique has grown popular for deciding whether a picture has been changed with a copy-move, splicing, or different forgery techniques. This paper present’s a novel passive image splicing forgery detection technique which includes three steps. Firstly, extracting an interest region (ROI) by using SURF descriptor (Speed up Robust Transform) Points is concerned based on the morphological procedure. Secondly, the Extraction of Mean-LBP (Mean Local Binary Pattern) Highlights on ROI is carried out. Lastly, a Classification of Mean- LBP characteristics is done with the SVM classifier. This novel method has shown the best outcome as SVM is used for classification. As well as it has shown a higher accuracy on the standard three datasets CASIA TIDE V1.0, CASIA TIDE V2.0, and Columbia University, revealed that current method has achieved higher accuracy of 97.9%, 98.2%, and 98.9% respectively. Finally, in terms of accuracy, the proposed SFD scheme outperformed the best recent works in this area.
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[26] Agarwal, S. and Chand, S. (2018) ‘Image forgery detection using co-occurrence-based texture operator in frequency domain’, Advances in Intelligent Systems and Computing, pp. 117–122. doi: 10.1007/978-981-10-3373-5_10.
[27] Alahmadi, A. et al. (2017) ‘Passive detection of image forgery using DCT and local binary pattern’, Signal, Image and Video Processing. Springer, 11(1), pp. 81–88.
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[30] Zhao, X. et al. (2011) ‘Passive detection of image splicing using conditional co-occurrence probability matrix’, APSIPA ASC 2011 - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011.
[2] Farid, H. (2009) ‘Exposing digital forgeries from JPEG ghosts’, IEEE transactions on information forensics and security. IEEE, 4(1), pp. 154–160.
[3] Ardizzone, E., Bruno, A. and Mazzola, G. (2010) ‘Copy-move forgery detection via texture description’, Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence - MiFor ’10, p. 59. doi: 10.1145/1877972.1877990.
[4] Sridevi, M., Mala, C. and Sanyam, S. (2012) ‘Comparative study of image forgery and copy-move techniques’, Advances in Intelligent and Soft Computing, 166 AISC(VOL. 1), pp. 715–723. doi: 10.1007/978-3-642-30157-5_71.
[5] Yatziv, L., & Sapiro, G. (2006). Fast image and video colorization using chrominance blending. IEEE transactions on image processing, 15(5), 1120-1129.
[6] Tai, Y. H., Chuang, L., & Chen, C. H. (2001). U.S. Patent Application No. 09/444,141.
[7] Hsu, J. C., Lee, H. L., & Chang, W. J. (2007). Flexural vibration frequency of atomic force microscope cantilevers using the Timoshenko beam model. Nanotechnology, 18(28), 285503.
[8] Qu, Z., Qiu, G. and Huang, J. (2009) ‘Detect Digital Image Splicing with Visual Cues.’, Information Hiding, 5806, pp. 247–261. Available at: http://dblp.uni-trier.de/db/conf/ih/ih2009.html#QuQH09.
[9] Kong, H. and Box, P. O. (2010) ‘Proceedings of 2010 IEEE 17th International Conference on Image Processing IMAGE TAMPERING DETECTION BASED ON STATIONARY DISTRIBUTION OF MARKOV CHAIN National Laboratory of Pattern Recognition , Institute of Automation , Chinese Academy of Sciences ’, pp. 2101–2104.
[10] Zhao, T., Zhang, Z. N., Westenskow, P. D., Todorova, D., Hu, Z., Lin, T., ... & Clegg, D. O. (2015). Humanized mice reveal differential immunogenicity of cells derived from autologous induced pluripotent stem cells. Cell stem cell, 17(3), 353-359.
[11] Kakar, P., Sudha, N. and Ser, W. (2011) ‘Exposing digital image forgeries by detecting discrepancies in motion blur’, IEEE Transactions on Multimedia, 13(3), pp. 443–452. doi: 10.1109/TMM.2011.2121056.
[12] He, Z. et al. (2012) ‘Digital image splicing detection based on Markov features in DCT and DWT domain’, Pattern recognition. Elsevier, 45(12), pp. 4292–4299.
[13] Hussain, M. et al. (2013) ‘Image forgery detection using multi-resolution Weber local descriptors’, Eurocon 2013, (July), pp. 1570–1577. doi: 10.1109/EUROCON.2013.6625186.
[14] Su, B. et al. (2014) ‘Enhanced state selection Markov model for image splicing detection’, EURASIP Journal on wireless communications and networking. Springer, 2014(1), p. 7.
[15] El-Latif, A. et al. (2019) ‘A Passive Approach for Detecting Image Splicing using Deep Learning and Haar Wavelet Transform.’, International Journal of Computer Network & Information Security, 11(5).
[6] Abrahim, A. R., Rahim, M. S. M., & Sami, A. S. (2020). Image Splicing Forgery Detection Scheme Using New Local Binary Pattern Varient. Academic Journal of Nawroz University, 9(3), 208–215.
[17] Kadam, K., Ahirrao, S., Kotecha, K., & Sahu, S. (2021). Detection and Localization of Multiple Image Splicing using MobileNet V1. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3130342
[18] Zandi, M., Mahmoudi-Aznaveh, A. and Mansouri, A. (2014) ‘Adaptive matching for copy-move Forgery detection’, in Information Forensics and Security (WIFS), 2014 IEEE International Workshop on, pp. 119–124.
[19] Bay, H., Tuytelaars, T., & Van Gool, L. (2006, May). Surf: Speeded up robust features. In European conference on computer vision (pp. 404-417). Springer, Berlin, Heidelberg.
[20] Lindeberg, T. (1998). Feature detection with automatic scale selection. International journal of computer vision, 30(2), 79-116.
[21] Shivakumar, B. L., & Baboo, S. S. (2011). Detection of region duplication forgery in digital images using SURF. International Journal of Computer Science Issues (IJCSI), 8(4), 199.
[22] Mikolajczyk, K. and Schmid, C. (2001) ‘Indexing based on scale invariant interest points’, in Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp. 525–531.
[23] Serra, J. (1982). Morphology for Grey-Tone Functions. Image analysis and mathematical morphology, 424-478.
[24] Haralick, R. M., Sternberg, S. R., & Zhuang, X. (1987). Image analysis using mathematical morphology. IEEE transactions on pattern analysis and machine intelligence, (4), 532-550.
[25] Maragos, P., & Schafer, R. W. (1987). Morphological filters--Part II: Their relations to median, order-statistic, and stack filters. IEEE Transactions on acoustics, speech, and signal processing, 35(8), 1170-1184.
[26] Agarwal, S. and Chand, S. (2018) ‘Image forgery detection using co-occurrence-based texture operator in frequency domain’, Advances in Intelligent Systems and Computing, pp. 117–122. doi: 10.1007/978-981-10-3373-5_10.
[27] Alahmadi, A. et al. (2017) ‘Passive detection of image forgery using DCT and local binary pattern’, Signal, Image and Video Processing. Springer, 11(1), pp. 81–88.
[28] Li, C. et al. (2017) ‘Image splicing detection based on Markov features in QDCT domain’, Neurocomputing. Elsevier, 228, pp. 29–36.
[29] Manu, V. T. and Mehtre, B. M. (2017) ‘Blind technique using blocking artifacts and entropy of histograms for image tampering detection’, in Second International Workshop on Pattern Recognition, p. 104430T.
[30] Zhao, X. et al. (2011) ‘Passive detection of image splicing using conditional co-occurrence probability matrix’, APSIPA ASC 2011 - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011.
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Published
2021-12-30
How to Cite
Alsandi, N. S. A. (2021). Image Splicing Detection Scheme Using Surf and Mean-LBP Based Morphological Operations. Science Journal of University of Zakho, 9(4), 178–183. https://doi.org/10.25271/sjuoz.2021.9.4.866
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