Enhanced Fiilltterr-based SIFT Apprroach fforr Copy-Move Forrgerry Dettecttiion

Document Type : Original Article

Authors

1 Dept. of Informatics, Electronics Research Institute.

2 Dept. of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University.

3 Dept. of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University.

4 Dept. of Information Technology, College of Computers and Information Technology, Taif University

Abstract

Image forgeries are applied to give the digital images other
meanings or to deceive the viewers. Image forgeries appear in
many cases such as judges in courts, cybercrimes, military and
intelligence deception, or defamation of important characters.
There are many different types of image forgeries such as copy
move forgery, image retouching, image splicing, image morphing,
and image resampling. Copy move forgery is the widest type and
easy to apply between all digital image forgeries. Scale Invariant
Features Transform (SIFT) algorithm is used strongly to detect
copy move forgeries due to its efficiency in digital image analysis.
SIFT algorithm is extracting image features, which are invariant to
geometrical transformations such as scaling, translation, and
rotation. These features are used in performing the matching
between different views of a scene or an object. This paper
enhances the efficiency of using SIFT algorithm in detecting copy
move forgery by two ways. Firstly, it enhances the image itself by
applying different types of digital filters to reinforce the image
features giving the ability to detect forgeries. Butterworth low-pass
filter, a high-pass filter, and the combination of them are applied
to this task. Secondly, the matching strategy is adapted based on
a new thresholding approach to increase the true positive rate
and decrease the false positive rate. Experimental results show
that the proposed approach gives better results compared with
traditional copy-move detection approaches. In addition, it gives better stability and reliability to different copy-move forgery conditions.

"> [1] D. David, Divya B., "Image Authentication Techniques and Advances
Survey", COMPUSOFT, An international journal of advanced computer
technology, vol. IV, Issue IV, April 2015.
[2] D. Usha Nandini, S. Divya, "A literature survey on various watermarking
techniques", Inventive Systems and Control (ICISC), 2017 International
Conference on, Coimbatore, India, 19-20 January 2017
[3] Osamah M. Al-Qershi, Bee Ee Khoo, "Passive detection of copy-move
forgery in digital images: State-of-the-art", Forensic Science International,
284 – 295, 3 July 2013.
[4] Judith A. Redi, Wiem Taktak, Jean-Luc Dugelay, "Digital image forensics:
a booklet for beginners", Multimedia Tools Appl., vol. 51, pp. 133-162,
2011.
[5] Gajanan K. Birajdar, Vijay H. Mankar, "Digital image forgery detection
using passive techniques: A survey", Digital Investigation, pp. 226-245,
2013.
[6] M. Ali Qureshi, M.Deriche, "A Review on Copy Move Image Forgery
Detection Techniques", Multi-Conference on Systems, Signals & Devices
(SSD), pp. 11-14, February 2014.
[7] Hany Farid, "Image Forgery Detection A survey", IEEE SIGNAL
PROCESSING MAGAZINE, March 2009.
; "> 8] M. Zimba, S. Xingming, "Fast and Robust Image Cloning Detection using
Block Characteristics of DWT Coefficients", International Journal of
Digital Content Technology and its Applications, vol. 5, Number 7, 2011.
[9] Manjima Mishra, Preeti Rai, "A Proposed Work on Image Forgery Detection Technique", International Journal of Computer Applications, vol. 163(2), April 2017.
[10] J. Zhao, J. Guo, "Passive forensics for copy-move image forgery using a
method based on DCT and SVD", Forensic Science International, vol. 33,
pp. 158-166, 2013.
[11] Seung-Jin Ryu, Matthias Kirchner, Min-Jeong Lee, and Heung-Kyu Lee,
"Rotation Invariant Localization of Duplicated Image Regions Based on
Zernike Moments", IEEE TRANSACTIONS ON INFORMATION
FORENSICS AND SECURITY, VOL. 8(8), August 2013.
[12] Muhammad Hussain, Sahar Q. Saleh, Hatim Aboalsamh, Ghulam
Muhammad, George Bebis, "Comparison between WLD and LBP
Descriptors for Non-intrusive Image Forgery Detection", IEEE
International Symposium on Innovations in Intelligent Systems and
Applications (INISTA) Proceedings, pp. 197-204, Alberobello, 23 – 25
June 2014.
[13] Devanshi Chauhan, Dipali Kasat, Sanjeev Jain, Vilas Thakare, "Survey On
Keypoint Based Copy-move Forgery Detection Methods On Image",
Procedia Computer Science, International Conference on Computational
Modeling and Security, pp. 206-212, 2016.
[14] O. M. Al-Qershi , B. E. Khoo, "Passive detection of copy-move forgery in
digital images: State-of-the-art", Forensic Science International, pp. 284 –
295, 2013.
[15] D. G. Lowe, "Object Recognition from Local Scale-Invariant Features",
Proc. of the International Conference on Computer Vision, vol. 2, pp.
1150-1157, 1999.
[16] D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints",
International Journal of Computer Vision, vol. 60(2), pp. 91-110, 2004.
[17] H. Huang, W. Guo, Y. Zhang, "Detection of Copy-Move Forgery in
Digital Images Using SIFT Algorithm," IEEE Pacific-Asia Workshop on
Computational Intelligence and Industrial Application, DOI
10.1109/PACIIA.2008.
[18] X. Pan, S. Lyu, "Region Duplication Detection Using Image Feature
Matching", IEEE TRANSACTIONS ON INFORMATION FORENSICS
AND SECURITY, vol. 5(4), 2010.
[19] J. Beis and D. Lowe, "Shape indexing using approximate nearest neighbor
search in high dimensional spaces", Proc. of CVPR, San Juan, 1997.
[20] M. F. Hashmi, A. R. Hambarde, A. G. Keskar, "Copy Move Forgery
Detection using DWT and SIFT Features", 3th International Conference on
Intelligent Systems Design and Applications (ISDA), pp. 188-193, 2013