Abstract
In this chapter, we present a copy-move forgery detection technique which utilizes Undecimated Dyadic Wavelet Transform (DyWT) for its operation. Dyadic Wavelet Transform (DyWT) is advantageous because it does not involve signal down-sampling and hence provides a better approximation compared to other wavelet transforms. Subsequently, we propose a technique to reduce the number of false positives obtained through application of this technique, to further improve the efficiency of the proposed scheme.
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
References
Androutsos, D., Plataniotiss, K.N., Venetsanopoulos, A.N.: Distance measures for color image retrieval. In: Proceedings 1998 International Conference on Image Processing, 1998. ICIP 98, vol. 2, pp. 770–774. IEEE (1998)
Bayram, S., Sencar, H.T., Memon, N.: An efficient and robust method for detecting copy-move forgery. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. ICASSP 2009, pp. 1053–1056. IEEE (2009)
Dixit, R., Naskar, R.: Dywt based copy-move forgery detection with improved detection accuracy. In: 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 133–138. IEEE (2016)
Fridrich, A.J., Soukal, B.D., Lukáš, A.J.: Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop. Citeseer (2003)
Li, G., Wu, Q., Tu, D., Sun, S.: A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 1750–1753. IEEE (2007)
Mahdian, B., Saic, S.: Using noise inconsistencies for blind image forensics. Image Vis. Comput. 27(10), 1497–1503 (2009)
Muhammad, G., Hussain, M., Bebis, G.: Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digit. Investig. 9(1), 49–57 (2012)
University of Granada: Color test images dataset. http://decsai.ugr.es/cvg/dbimagenes/c256.php
SIPI: Image dataset. http://sipi.usc.edu/database/database.php?volume=misc
Zhang, J., Feng, Z., Su, Y.: A new approach for detecting copy-move forgery in digital images. In: 11th IEEE Singapore International Conference on Communication Systems, 2008. ICCS 2008, pp. 362–366. IEEE (2008)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Roy, A., Dixit, R., Naskar, R., Chakraborty, R.S. (2020). Copy-Move Forgery Detection in Transform Domain. In: Digital Image Forensics. Studies in Computational Intelligence, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-10-7644-2_6
Download citation
DOI: https://doi.org/10.1007/978-981-10-7644-2_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7643-5
Online ISBN: 978-981-10-7644-2
eBook Packages: EngineeringEngineering (R0)