Abstract
Digital image authenticity is always an imperative question to tackle whenever a digital image is being assessed for its content. Using digital forensic algorithms, the image will be evaluated for various traces left from numerous categories of manipulations including, among others, copy–move operations. Later this is considered an essential block in most digital image forgeries. It results in changing the information incorporated in a scene, hiding information from an image, or emphasizing some parts of the image. In this paper we propose and investigate two main approaches that differ in the feature extraction process in order to detect copy–move traces. In the first method, we use two-dimensional discrete cosine transform. Whereas in the second method, the phase response of Gabor filter is being used. Instead of being applied on the image directly, the two methods are applied over the first, the second or the third principal component of the image after being divided into overlapping blocks. Combining these conditions results in six basic implementations that are investigated under three parameters that must be optimized: block dimension, contrast and similarity thresholds. Results from testing and validation process demonstrate that the highest performance, in terms of false accept rate, is obtained when using Gabor filter associated with the first principal component of the image outperforming a reference method we implemented as well.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Farid, H. (2009). Image forgery detection, a survey. IEEE Signal Processing Magazine, 26(2), 16–26.
Fridrich, J., Soukal, D., & Lukas, J. (2003). Detection of copy–move forgery in digital images. In Proceedings of digital forensic research workshop.
Li, L., Li, S., & Hancheng, Z. (2013). 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.
Bayram, S., Sencar, H.T., & Memon, N. (2009). An efficient and robust method for detecting copy–move forgery. In IEEE international conference on acoustics, speech and signal processing (pp. 1053–1056).
Popescu, A. C., & Farid, H. (2004). Exposing digital forgeries by detecting duplicated image regions. Technical Report, TR2004-515, Dartmouth College, Hanover, NH, USA.
Li, G., Wu, Q., Tu, D., & Sun S. (2007). A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. In Proceedings of the 8th IEEE international conference on multimedia and expo.
Jen-Chun, L. (2015). Copy–move image forgery detection based on Gabor magnitude. Journal of Visual Communication and Image Representation, 31, 320–334.
Yohannan, R. P., & Manuel, M. (2016). Detection of copy–move forgery based on Gabor filter. In IEEE international conference on engineering and technology (pp. 629–634).
Hsu, H. C., & Wang, M.S. (2012). Detection of copy–move forgery image using Gabor descriptor. In IEEE proceedings of the international conference on anti-counterfeiting, security and identification (pp. 1–4).
Mohan, M., & Preetha, V. H. (2017). Gabor filter—HOG based copy move forgery detection. Journal of Electronics and Communication Engineering, 2, 41–45.
Diane, W. N. N., Xingming, S., & Moise, F. K. (2014). A survey of partition-based techniques for copy–move forgery detection. The Scientific World Journal. https://doi.org/10.1155/2014/975456.
Christlein, V., Riess, C., Jordan, J., Riess, C., & Angelopoulou, E. (2012). An evaluation of popular copy–move forgery detection approaches. IEEE Transactions on Information Forensics and Security, 7(6), 1841–1854.
Shashi Kumar, D. R., Raja, K. B., Nuthan, N., Sindhuja, B., Supriya, P., Chhotaray, R. K., & Pattnaik, S. (2011). Iris recognition based on DWT and PCA. In 11 international conference on computational intelligence and communication networks (pp. 489–493).
Kaçar, U., Kirci, M., Güneş, E. O., & Inan, T. (2015). A comparison of PCA, LDA and DCVA in ear biometrics classification using SVM. In 23nd signal processing and communications applications conference (SIU) (pp. 1260–1263).
Victor, B., Bowyer, K., & Sarkar, S. (2002). An evaluation of face and ear biometrics. Object recognition supported by user interaction for service robots, 1, 429–432.
Khan, S., & Kulkarni, A. (2010). Reduced time complexity for detection of copy–move. International Journal of Computer Applications, 6(7), 31–36.
Daugman, J. (1988). Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression”. IEEE Transcation Acoustic, Speech, Signal Processing, 36(7), 1169–1179.
Masek, L. (2003). Recognition of human iris patterns for biometric identification. Master’s thesis presented at the University of Western Australia, Australia.
Hilal, A. Système d’identification à partir de l’image d’iris et détermination de la localisation des informations. Ph.D. thesis presented at the University of Technology of Troyes, France. www.theses.fr/2013TROY0021.pdf
Acknowledgement
This work was supported by a Grant from the Lebanese University.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hilal, A., Chantaf, S. Uncovering copy–move traces using principal component analysis, discrete cosine transform and Gabor filter. Analog Integr Circ Sig Process 96, 283–291 (2018). https://doi.org/10.1007/s10470-018-1139-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10470-018-1139-z