Abstract
In today’s cyber world images and videos are the major sources of information exchange. The authenticity of digital images and videos is extremely crucial in the legal industry, media world and broadcast industry. However, with huge proliferation of low-cost, easy–to–use image manipulating software the fidelity of digital images is at stake. In this paper we propose a technique to detect digital forgery in JPEG images, based on ”double–compression”. We deal with JPEG images because JPEG is the standard storage format used in almost all present day digital cameras and other image acquisition devices. JPEG compresses an image to optimize the storage space requirement. When an attacker or criminal alters some part of a JPEG image by any image–editing tool and rewrites it to memory, the forged or modified part gets doubly–compressed. In this paper, we exploit this double–compression in JPEG images to identify digital forgery.
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References
Sencar, H.T., Memon, N. (eds.): Digital Image Forensics: There is More to a Picture than Meets the Eye. Springer, New York (2013)
Redi, J., Taktak, W., Dugelay, J.L.: Digital Image Forensics: A Booklet for Beginners. Multimedia Tools and Applications 51(1), 133–162 (2011)
Wallace, G.: The JPEG still picture compression standard. IEEE Transactions on Consumer Electronics 34(4), 30–44 (1991)
Sencar, H.T., Memon, N.: Overview of state-of-the-art in digital image forensics. Indian Statistical Institute Platinum Jubilee Monograph series titled Statistical Science and Interdisciplinary Research. World Scientific, Singapore (2008)
Wu, J., Kamath, M.V., Poehlman, S.: Detecting differences between photographs and computer generated images. In: Proceedings of the 24th IASTED International conference on Signal Processing, Pattern Recognition, and Applications, pp. 268–273 (2006)
Manjunath, B.S., Ohm, J.R., Vasudevan, V.V., Yamada, A.: Color and Texture Descriptors. IEEE Trans. Circuits and Systems for Video Technology 11(6), 703–715 (2001)
Lalonde, J.F., Efros, A.A.: Using color compatibility for assessing image realism. In: Proceedings of the International Conference on Computer Vision (2007)
Wang, N., Doube, W.: How real is really a perceptually motivated system for quantifying visual realism in digital images. In: Proceedings of the IEEE International Conference on Multimedia and Signal Processing, pp. 141–149 (2011)
Ng, T.T., Chang, S.F.: Classifying photographic and photorealistic computer graphic images using natural image statistics. Technical report, ADVENT Technical Report, Columbia University (2004)
Farid, H., Lyu, S.: Higher–order wavelet statistics and their application to digital forensics. In: IEEE Workshop on Statistical Analysis in Computer Vision (2003)
Srivastava, A., Lee, A.B., Simoncelli, E.P., Zhu, S.C.: On advances in statistical modeling of natural images. Journal of Mathematical Imaging 18(1), 17–33 (2003)
Fridrich, A.J., Soukal, B.D., Lukáš, A.J.: Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop (2003)
Popescu, A.C., Farid, H.: Exposing digital forgeries by detecting traces of re–sampling. IEEE Transactions on Signal Processing 53(2), 758–767 (2005)
Lowe, D.: Distinctive image features from scale–invariant key–points. Internaional Journal of Computer Vision 60(2), 91–110 (2004)
Pan, X., Lyu, S.: Detecting image duplication using SIFT features. In: Proceedings of IEEE ICASSP (2010)
Huang, H., Guo, W., Zhang, Y.: Detection of copy–move forgery in digital images using SIFT algorithm. In: IEEE Pacific–Asia Workshop on Computational Intelligence and Industrial Application (2008)
Farid, H.: Exposing digital forgeries from JPEG ghosts. IEEE Transactions on Information Forensics and Security 4(1), 154–160 (2009)
Zach, F., Riess, C., Angelopoulou, E.: Automated Image Forgery Detection through Classification of JPEG Ghosts. In: Proceedings of the German Association for Pattern Recognition (DAGM 2012), pp. 185–194 (August 2012)
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Malviya, P., Naskar, R. (2014). Digital Forensic Technique for Double Compression Based JPEG Image Forgery Detection. In: Prakash, A., Shyamasundar, R. (eds) Information Systems Security. ICISS 2014. Lecture Notes in Computer Science, vol 8880. Springer, Cham. https://doi.org/10.1007/978-3-319-13841-1_25
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DOI: https://doi.org/10.1007/978-3-319-13841-1_25
Publisher Name: Springer, Cham
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