Abstract
In the present digital world integrity and trustworthiness of the digital images is an important issue. And most probably copy- move forgery is used to tamper the digital images. Thus as a solution to this problem, through this paper we proposes a unique and blind method for detecting copy-move forgery using dyadic wavelet transform (DyWT) in combination with scale invariant feature transform (SIFT). First we applied DyWT on a given test image to decompose it into four sub-bands LL, LH, HL, HH. Out of these four sub-bands LL band contains most of the information we intended to apply SIFT on LL part only to extract the key features and using these key features we obtained descriptor vector and then went on finding similarities between various descriptors vector to come to a decision that there has been some copy-move tampering done to the given image. In this paper, we have done a comparative study based on the methods like (a).DyWT (b).DWT and SIFT (c). DyWT and SIFT. Since DyWT is invariant to shift whereas discrete wavelet transform (DWT) is not, thus DyWT is more accurate in analysis of data. And it is shown that by using DyWT with SIFT we are able to extract more numbers of key points that are matched and thus able to detect copy-move forgery more efficiently.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Muhammad, N., Hussain, M., Muhammad, G., Bebis, G.: Copy-move forgery detection using dyadic wavelet transform. In: Proceedings of IEEE Eighth International Conference on Computer Graphics, Imaging and Visualization (CGIV 2011), pp. 103–108 (2011)
Jing, L., Shao, C.: Image Copy-Move Forgery Detecting Based on Local Invariant Feature. Journal of Multimedia 7(1), 90–97 (2012)
Muhammad, G., Hussain, M., Khawaji, K., Bebis, G.: Blind copy move image forgery detection using dyadic undecimated wavelet transform. In: Proceedings of IEEE 17th International Conference on Digital Signal Processing (DSP 2011), pp. 1–6 (2011)
Fridrich, A.J., Soukal, B.D., Lukáš, A.J.: Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop, Cleveland, OH, USA, pp. 55–61 (August 2003)
Popescu, A.C., Farid, H.: Exposing digital forgeries by detecting duplicated image regions, Department of Computer Science, Dartmouth College. Technical Report. TR2004-515 (August 2004)
Li, G., Wu, Q., Tu, D., Sun, S.: A sorted neighbourhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. In: Proceedings of IEEE International Conference on Multimedia and Expo, pp. 1750–1753 (2007)
Bayram, S., Sencar, H.T., Memon, N.: An efficient and robust method for detecting copy-move forgery. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2009), pp. 1053–1056 (2009)
Kang, X., Wei, S.: Identifying tampered regions using singular value decomposition in digital image forensics. Proceedings of the IEEE International Conference on Computer Science and Software Engineering 3, 926–930 (2008)
Huang, H., Guo, W., Zhang, Y.: Detection of copy-move forgery in digital images using SIFT algorithm. In: Proceedings of Pacific-Asia Workshop on Computational Intelligence and Industrial Application (PACIIA 2008), vol. 2, pp. 272–276 (2008)
Amerini, I., Ballan, L., Caldelli, R., Bimbo, A.D., Serra, G.: A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE Transactions on Information Forensics and Security 6(3), 1099–1110 (2011)
Amerini, I., Ballan, L., Caldelli, R., Bimbo, A.D., Tongo, L.D., Serra, G.: Copy-move forgery detection and localization by means of robust clustering with J-linkage. Signal Processing: Image Communication 28, 659–669 (2013)
Mallat, S., Zhong, S.: Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(7), 710–732 (1992)
Muhammad, G., Hussain, M., Bebis, G.: Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digital Investigation 9(1), 49–57 (2012)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Al-Qershi, O.M., Khoo, B.E.: Passive detection of copy-move forgery in digital images: State-of-the-art. Forensic Science International 231(1), 284–295 (2013)
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. Journal of Information Hiding and Multimedia Signal Processing 4(1), 46–56 (2013)
Birajdar, G.K., Mankar, V.H.: Digital image forgery detection using passive techniques: A survey. Digital Investigation 10(3), 226–245 (2013)
Devi Mahalakshmi, S., Vijayalakshmi, K., Priyadharsini, S.: Digital image forgery detection and estimation by exploring basic image manipulations. Digital Investigation 8(3), 215–225 (2012)
Hashmi, M.F., Hambarde, A.R., Keskar, A.G.: Copy Move Forgery Detection using DWT and SIFT Features. In: Proceeding of 13th IEEE International Conference on Intelligent Systems Design and Applications (ISDA 2013), pp. 188–193 (December 2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Anand, V., Hashmi, M.F., Keskar, A.G. (2014). A Copy Move Forgery Detection to Overcome Sustained Attacks Using Dyadic Wavelet Transform and SIFT Methods. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8397. Springer, Cham. https://doi.org/10.1007/978-3-319-05476-6_54
Download citation
DOI: https://doi.org/10.1007/978-3-319-05476-6_54
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05475-9
Online ISBN: 978-3-319-05476-6
eBook Packages: Computer ScienceComputer Science (R0)