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Image Forensic Investigation Using Discrete Cosine Transform-Based Approach

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Abstract

Digital image tempering is widespread because software and devices that manipulate image information are easily available for high-performance image editing. Now everything is online and digital images are presented as evidence of any event, documentation where forgery hides its traces. Existing techniques for forgery detection are based on the higher complexity of computational costs. The technique proposed is robust even with pre-and post-processing operations for automatic detection and localization of specific artifacts. A proposed methodology using the Discrete Cosine Transform technique was used to obtain features from each block of images that reduce the block dimension. Tampered blocks of images are compared with predefined threshold values based on robust parameters to detect similar blocks in reduced time. Experimental results show multiple forgery detection with low computational complexity and retained significance of image information. On several images that are affected by different forgery types, the proposed method acts robustly.

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References

  1. Fridrich, J., Soukal, B. D. & Lukáš, A. J. (2003). Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop.

  2. Popescu, A. C. & Farid, H. (2004). Exposing digital forgeries by detecting duplicated image regions, Tech. Rep. TR2004–515, Dartmouth College.

  3. Luo, W., Huang, J. & Qiu, G. (2006). Robust detection of region duplication forgery in digital image. In: Proceedings of the 18th International Conference on Pattern Recognition, vol. 4, pp. 746–749.

  4. Mahdian, B., & Saic, S. (2007). Detection of copy-move forgery using a method based on blur moment invariants. Forensic Science International, 171, 180–189.

    Article  Google Scholar 

  5. Kang, X. & Wei, S. (2008). Identifying tampered regions using singular value decomposition in digital image forensics. In: Proceedings of International onference on Computer Science and Software Engineering, pp. 926–930

  6. Bayram, S., Husrev, H. T. & Memon, N. (2009). An efficient and robust method for detecting copy-move forgery. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1053–1056.

  7. Lin, H., Wang, C., & Kao, Y. (2009). Fast copy-move forgery detection. WSEAS Transactions on Signal Processing, 5(5), 188–197.

    Google Scholar 

  8. Ryu, S., Lee, M. & Lee, H. (2010) Detection of copy-rotate-move forgery using Zernike moments. In: Information Hiding Conference, June 2010, pp. 51–65.

  9. Amerini, I., Balla, L., Caldelli, R., Del-Bimbo, A., & Serra, G. (2011). A sift-based forensic method for copy-move attack detection and transformation recovery. IEEE Transactions on Information Forensics and Security, 6(3), 1099–1110.

    Article  Google Scholar 

  10. Zhao, J., & Guo, J. (2013). Passive forensics for copy-move image forgery using a method based on DISCRETE COSINE TRANSFORM and SVD. Forensic Science International, 233, 158–166.

    Article  Google Scholar 

  11. Chang, I., Yu, J. C., & Chang, C. (2013). A forgery detection algorithm for exemplar-based inpainting images using multi-region relation. Imavis, 31(1), 57–71.

    MathSciNet  Google Scholar 

  12. Cozzolino, D., Poggi, G., & Verdoliva, L. (2015). Efficient dense-field copy–move forgery detection. IEEE Transactions on Information Forensics and Security, 10, 2284–2297.

    Article  Google Scholar 

  13. Pan, X., & Lyu, S. (2010). Region duplication detection using image feature matching. IEEE Transactions on Information Forensics and Security, 4, 857–867.

    Article  Google Scholar 

  14. Alexandra, G. & Lihi Zelnik, M. (2013). SIFTpack: A compact representation for efficient sift matching. In: IEEE International Conference on Computer Vision, 777–784.

  15. Shivakumar, B. L., & Baboo, S. (2011). Detection of region duplication forgery in digital images using SURF. International Journal of Computer Science, 8, 199–205.

    Google Scholar 

  16. Chen, L., Lu, W., Ni, J., Sun, W., & Huang, J. (2013). Region duplication detection based on Harris corner points and step sector statistics. Journal of Visual Communication and Image Representation, 24, 244–254.

    Article  Google Scholar 

  17. Silva, E., Carvalho, T., Ferreira, A., & Rocha, A. (2015). Going deeper into copy-move forgery detection: Exploring image telltales via multi-scale analysis and voting processes. Journal of Visual Communication and Image Representation, 29, 16–32.

    Article  Google Scholar 

  18. Daugman, J. (1980). Two-dimensional analysis of cortical receptive field profiles. Vision Research, 20, 846–856.

    Article  Google Scholar 

  19. Huang, Y., Lu, W., Sun, W., & Long, D. (2011). Improved DISCRETE COSINE TRANSFORM-based detection of copy-move forgery in images. Forensic Science International, 206(1–3), 178–184.

    Article  Google Scholar 

  20. Liu, G., Wang, J., Lian, S., & Wang, Z. (2011). A passive image authentication scheme for detecting region-duplication forgery with rotation. Journal of Network and Computer Applications, 34, 1557–1565.

    Article  Google Scholar 

  21. Myrna, A. N., Venkateshmurthy, M. G. & Patil, C. G. (2007) Detection of region duplication forgery in digital images using wavelets and log-polar mapping. In: Conference on Computational Intelligence and Multimedia Applications, Vol. 3, pp. 371–377.

  22. Wu, Q., Wang, S., & Zhang, X. (2010). Detection of image region-duplication with rotation and scaling tolerance. Lecture Notes in Computer Science, 6421, 100–109.

    Article  Google Scholar 

  23. Wang, Y., Gurule, K., Wise, J. & Zheng, J. (2012) Wavelet based region duplication forgery detection. Information technology: New generations (ITNG). In: 2012 Ninth International Conference. pp. 30–35.

  24. Zhang, C., Guo, X., & Cao, X. (2010). Duplication localization and segmentation. Lecture Notes in Computer Science, 6297, 578–613.

    Article  Google Scholar 

  25. Xu, B., Wang, J., Liu, G., Li, H. & Dai, Y. (2010). Image copy-move forgery detection based on surf. In: International Conference on Multimedia Information Networking and Security. pp. 889–895.

  26. Lee, J. (2015). Copy-move image forgery detection based on gabor magnitude. Journal of visual communication and image representation, 31, 320–334.

    Article  Google Scholar 

  27. Lin, S. D. & Wu, T. (2011) An integrated technique for splicing and copy-move forgery image detection. In: 2011 4th International Congress on Image and Signal Processing (CISP), pp. 1086–1090.

  28. Li, J., Li, X., Yang, B., & Sun, X. (2015). Segmentation-based image copy-move forgery detection scheme. IEEE Transactions on Information Forensics and Security, 10, 507–518.

    Article  Google Scholar 

  29. Qu, Z., Luo, W. & Huang, J. (2008) A convolutive mixing model for shifted double JPEG compression with application to passive image authentication. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1661–1664.

  30. Bashar, M., Noda, K., Ohnishi, N. & Mori, K. (2010). Exploring duplicated regions in natural images. In: IEEE Transaction on Image Processing.

  31. Lin, Z. C., Wang, R. R., Tang, X. O. & Shum, H. Y. (2005) Detecting doctored images using cam- era response normality and consistency. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1087–1092.

  32. Li, W., Yu, N. & Yuan, Y. (2008) Doctored JPEG image detection. In: IEEE International Con-ference on Multimedia and Expo, pp. 253–256.

  33. Christlein, V., Riess, C. & Angelopoulou, E. (2010) On rotation invariance in copy-move forgery detection. In: Proceedings of the IEEE WIFS, Seattle, WA, USA.

  34. Ustubioglu, B., Ulutas, G., Ulutas, M., & Nabiyev, V. V. (2016). A new copy move forgery detection technique with automatic threshold determination. AEU—International Journal of Electronics and Communications, 70(8), 1076–1087.

    Article  Google Scholar 

  35. Bi, X. & Pun, C. (2018). PT. Pattern recognition.

  36. Cao, Y., Gao, T., Fan, L., & Yang, Q. (2012). A robust detection algorithm for copy-move forgery in digital images. Forensic Science International, 214(1–3), 33–43.

    Article  Google Scholar 

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Authors and Affiliations

  1. MRIIRS, Faridabad, India

    Monika, Dipali Bansal & Abhiruchi Passi

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  1. Monika
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  2. Dipali Bansal
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  3. Abhiruchi Passi
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Correspondence to Monika.

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Monika, Bansal, D. & Passi, A. Image Forensic Investigation Using Discrete Cosine Transform-Based Approach. Wireless Pers Commun 119, 3241–3253 (2021). https://doi.org/10.1007/s11277-021-08396-1

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  • DOI: https://doi.org/10.1007/s11277-021-08396-1

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