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Block-based copy–move image forgery detection using DCT

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Abstract

Digital image forensic is a sub-area of multimedia security whose objective is to expose the malicious image manipulations in digital images. Among different types of image forgery, copy–move forgery is the most popular to forge the digital images where a part of the original digital image is copied and pasted at another position in the same image. Different methods have been developed to detect the image forgery in digital images. On the basis of our literature review, we identify that less attention is given to clustering algorithms to speed up the block matching strategy during image forgery detection process. Therefore, to address this issue, we present a pixel-based copy–move image forgery detection method to check the genuineness of digital images. Proposed method includes the following steps: (1) convert the color image into gray-scale image, (2) divide the gray-scale image into overlapping blocks of size 8 × 8, (3) feature extraction using DCT on the basis of different feature sets, (4) block clustering using K-means algorithm, and (5) radix sort for feature matching. Experimental results demonstrate that proposed method can efficiently detect the forged part from digital images.

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References

  1. Glinz, M.: On non-functional requirements. In: Proceedings of the 15th IEEE International requirements engineering conference (RE-2007), 21–26 (2007)

  2. Pathak, N., Singh, B.M., Sharma, G.: UML 2.0 based framework for the development of secure web application. BVICAM’s Int. J. Inf. Technol. Springer 9(1), 101–109 (2017)

    Google Scholar 

  3. Mahfuth, A., Yussof, S., Baker, A.A., and Ali N.: A systematic literature review: information security culture. In proceedings: IEEE International Conference on Research and Innovation in Information Systems, pp. 1–6 (2017)

  4. Huang, Z., Liu, S., Mao, X., Chen, K., Li, J.: Insight of the protection for data security under selective opening attacks. Inf. Sci. 412–413, 223–241 (2017)

    Article  Google Scholar 

  5. Stergiou, C., Psannis, K.E., Kim, B.-G., Gupta, B.: Secure integration of IoT and cloud computing. Future Gener Comput Syst 78, 964–975 (2018)

    Article  Google Scholar 

  6. Qureshi, M.A., Deriche, M.: A bibliography of pixel-based blind image forgery detection techniques. Signal Process Image Commun 39, 46–74 (2015)

    Article  Google Scholar 

  7. Parveen, A., Tayal, A.: An algorithm to detect the forged part in an image. In: IEEE International Conference on Communication and Signal Processing, 1486–1490 (2016)

  8. Farid, H.: Image forgery detection: a survey. IEEE Signal Process. Mag. 36(2), 16–25 (2009)

    Article  Google Scholar 

  9. Photo Tampering Throughout History, http://pth.izitru.com/1860_13_00.html

  10. Parveen, A., Khan, Z.H., and Ahmad, S.N.: Pixel based copy-move image forgery detection techniques: a systematic literature review. In: Proceedings of the 5th IEEE International Conference on Computing for Sustainable Global Development, organized by BVICAM, New Delhi, India (2018)

  11. Fridrich, J., Soukal, D., and Lukas, J.: Detection of copy–move forgery in digital images. Digital Image Forensic Workshop, 1–10 (2003)

  12. Popescu, A.C., Farid, H.: Exposing digital forgeries for detecting duplicated regions. Department of Computer Science, Dartmouth College, Technical Report (2004)

  13. Fadl, S.M., Semary, N.A.: A proposed accelerated image forgery copy–move forgery detection. In: Proceedings of Visual Communications and Image Processing Conference, pp. 253–257 (2014)

  14. Christlein, V., Riess, C., Jordan, J., Riess, C., Angelopoulou, E.: An evaluation of popular copy–move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)

    Article  Google Scholar 

  15. Kitchenham, B.: Procedure for performing systematic review. Joint Technical Report, Software Engineering Group, Department of Computer Science, Keele University, United Kingdom and Empirical Software Engineering, National ICT Australia (2004)

  16. Preeti, Kumar, D.: Feature selection for face recognition using DCT-PCA and bat algorithm. BVICAM’s Int. J. Inf. Technol. 9(4), 411–423 (2017)

    Google Scholar 

  17. Jain, S.: Classification of protein Kinase B using discrete wavelet transform. BVICAM’s Int. J. Inf. Technol. 10(2), 211–216 (2018)

    Google Scholar 

  18. Ghorbani, M., Firouzmand, M., Faraahi, A.: DWT-DCT (QCD) based copy–move image forgery detection. In: Proceedings of International Conference on systems, signals, and image processing (2011)

  19. Huang, Y., Lu, W., Sun, W., Long, D.: Improved DCT-based detection of copy–move forgery in images. Forensic Sci. Int. 206(1–3), 178–184 (2011)

    Article  Google Scholar 

  20. Hu, J., Zhang, H., Gao, Q., Huang, H.: An improved lexicographical sort algorithm of copy-move forgery detection. In: IEEE Proceedings of 2nd International Conference on Networking and Distributed Computing, pp. 23–27 (2011)

  21. Cao, Y., Gao, T., Fan, L., Yang, Q.: A robust detection algorithm for copy move forgery in digital images. Forensic Sci. Int. 214(1–3), 33–43 (2012)

    Article  Google Scholar 

  22. Wandji, N.D., Xingming, S., Kue, M.F.: Detection of copy-move forgery in digital images based on DCT. Cornell University Library, New York, USA, arxiv.org/pdf/1308.5661 (2013)

  23. Kumar, S., Desai, J., Mukherjee, S.: A Fast DCT based method for copy move forgery detection. In: Proceedings of the IEEE 2nd International Conference on Image Information Processing, pp. 649–654 (2013)

  24. Zhao, J., Guo, J.: Passive forensics for copy–move image forgery using a method based on DCT and SVD. Forensic Sci. Int. 233(1–3), 158–166 (2013)

    Article  Google Scholar 

  25. Kumar, S., Desai, J,, Mukherjee, S.: DCT-PCA based method for copy-move forgery detection. In: Proceedings of 48th Annual Conventions of CSI-Volume-II, ASIC, Springer, pp. 577 (2014)

  26. Fadl, S.M., Semary, N.A., Hadhoud, M.M.: Fan search for image copy-move forgery detection. In: Proceedings of AMLTA, Springer, pp. 177–186 (2014)

  27. Alahmadi, A., Hussain, M., Aboalsamh, H., Muhammad, G., Bebis, G., Mathkour, H.: Passive detection of image forgery using DCT and local binary pattern. Signal Image Video Process 11(1), 81–88 (2017)

    Article  Google Scholar 

  28. Mahmood, T., Mahmood, Z., Shah, M., Saba, T.: 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 (2018)

    Article  Google Scholar 

  29. Kanan, C., Cottrell, G.W.: Color-to-grayscale: does the method matter in image recognition. PLoS ONE 7(1), 1–7 (2012)

    Article  Google Scholar 

  30. Saravanan, C.: Color image to grayscale image conversion, In: Proceedings of 2nd IEEE International Conference on computer engineering and applications, pp. 196–199 (2010)

  31. Ahmed, N., Nataranjan, T., Rao, K.R.: Discrete cosine transform. IEEE Trans. on Comput., 90–93 (1974)

  32. Kaushal, V., Garg, B., Jaiswal, A., Sharma, G.K.: Energy aware computation driven approximate DCT architecture for image processing. In: Proceedings of 28th International Conference on VLSI Design and 14th International Conference on embedded systems, pp. 357–362 (2015)

  33. Tjahyadi, R., Liu, W., Venkatesh, S.: Application of the DCT energy histogram for face recognition. In: Proceedings of 2nd International Conference on Information Technology for Application, pp. 305–310 (2004)

  34. Majumdar, K.: Human scalp EEG processing: various soft computing approaches. Appl. Soft Comput. 11, 4433–4447 (2011)

    Article  Google Scholar 

  35. Birvinskas, D., Jusas, V., Martisius, I., Damasevicius, R.: EEG dataset reduction and feature extraction using discrete cosine transform. In: Proceedings of 6th European Modeling Symposium, pp. 199–204 (2012)

  36. Arthur, D., Vassilvitskii, S.: K-means ++: the advantage of careful seeding. In: Proceedings of 18th Annual ACM-SIAM Symposium on Discrete Algorithm, pp. 1027–1035 (2007)

  37. Thareja, R.: Data structures using C, 2nd edn. Oxford University Press, Oxford (2014)

    Google Scholar 

  38. Elkan, C.: Using the triangle inequality to accelerate k-means. In: Proceedings of ICML, pp. 147–153 (2003)

  39. Zhang, Z., Wang, D., Wang, C., Zhou, X.: Detecting copy–move forgeries in images based on DCT and main transfer vector. KSII Trans. Internet Inf. Syst. 11(9), 4567–4587 (2017)

    Google Scholar 

  40. Parveen, A., Khan, Z.H., Ahmad, S.N.: Identification of the Forged Images using Image Forensic Tools. In Proceedings of 2nd International Conference on Communication and Computing Systems, CRC-Press, Taylor and Francis (2018)

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Correspondence to Azra Parveen.

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Parveen, A., Khan, Z.H. & Ahmad, S.N. Block-based copy–move image forgery detection using DCT. Iran J Comput Sci 2, 89–99 (2019). https://doi.org/10.1007/s42044-019-00029-y

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