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Robust image forgery detection using point feature analysis

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
Języki publikacji
EN
Abstrakty
EN
Day for day it becomes easier to temper digital images. Thus, people are in need of various forgery image detection. In this paper, we present forgery image detection techniques for two of the most common image tampering techniques; copy-move and splicing. We use match points technique after feature extraction process using SIFT and SURF. For splicing detection, we extracted the edges of the integral images of Y , Cb, and Cr image components. GLCM is applied for each edge integral image and the feature vector is formed. The feature vector is then fed to a SVM classifier. For the copy-move, the results show that SURF feature extraction can be more efficient than SIFT, where we achieved 80% accuracy of detecting tempered images. On the other hand, processing the image in YCbCr color model is found to give promising results in splicing image detection. We have achieved 99% true positive rate for detecting splicing images.
Rocznik
Tom
Strony
373–--380
Opis fizyczny
Bibliogr. 28 poz., il.
Twórcy
  • German University in Cairo, Cairo, Egypt
  • German University in Cairo, Cairo, Egypt
  • German University in Cairo
  • Ain Shams Univeristy
Bibliografia
  • 1. CASIA V.I,II, author=Jing Dong,Wei Wang,Tieniu Tan, howpublished = https://www.kaggle.com/sophatvathana/casia-dataset.
  • 2. Tim Adams, Jens Dörpinghaus, Marc Jacobs, and Volker Steinhage. Automated lung tumor detection and diagnosis in ct scans using texture feature analysis and svm. In FedCSIS Communication Papers, 2018. http://dx.doi.org/10.15439/2018F176.
  • 3. Maryam Nabil Al-Berry, Mohammed A.-M. Salem, Hala Mousher Ebeid, Ashraf S Hussein, and Mohammed F Tolba. Fusing directional wavelet local binary pattern and moments for human action recognition. IET Computer Vision, 10(2):153–162, 2016.
  • 4. Amani A Alahmadi, Muhammad Hussain, Hatim Aboalsamh, Ghulam Muhammad, and George Bebis. Splicing image forgery detection based on dct and local binary pattern. In 2013 IEEE Global Conference on Signal and Information Processing, pages 253–256. IEEE, 2013. http://dx.doi.org/10.1109/GlobalSIP.2013.6736863.
  • 5. Hesham A Alberry, Abdelfatah A Hegazy, and Gouda I Salama. A fast sift based method for copy move forgery detection. Future Computing and Informatics Journal, 3(2):159–165, 2018. http://dx.doi.org/10.1016/j.fcij.2018.03.001.
  • 6. Herbert Bay, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool. Speeded-up robust features (surf). Computer vision and image understanding, 110(3):346–359, 2008.
  • 7. Sevinc Bayram, Husrev Taha Sencar, and Nasir Memon. A survey of copy-move forgery detection techniques. pages 538–542, 2008. http://dx.doi.org/10.1109/ICISC.2017.8068703.
  • 8. Vincent Christlein, Christian Riess, Johannes Jordan, Corinna Riess, and Elli Angelopoulou. An evaluation of popular copy-move forgery detection approaches. IEEE Transactions on information forensics and security, 7(6):1841–1854, 2012. http://dx.doi.org/10.1109/TIFS.2012.2218597.
  • 9. A Jessica Fridrich, B David Soukal, and A Jan Lukáš. Detection of copy-move forgery in digital images. In in Proceedings of Digital Forensic Research Workshop. Citeseer, 2003. http://dx.doi.org/10.1016/j.forsciint.2013.05.027.
  • 10. Fahime Hakimi, Mahdi Hariri, and Farhad GharehBaghi. Image splicing forgery detection using local binary pattern and discrete wavelet transform. In 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), pages 1074–1077. IEEE, 2015. http://dx.doi.org/10.1109/KBEI.2015.7436195.
  • 11. Tu K Huynh, Khoa V Huynh, Thuong Le-Tien, and Sy C Nguyen. A survey on image forgery detection techniques. In The 2015 IEEE RIVF International Conference on Computing & Communication Technologies- Research, Innovation, and Vision for Future (RIVF), pages 71–76. IEEE, 2015. http://dx.doi.org/10.1109/RIVF.2015.7049877.
  • 12. Tu Huynh-Kha, Thuong Le-Tien, Synh Ha-Viet-Uyen, Khoa Huynh-Van, and Marie Luong. A robust algorithm of forgery detection in copy-move and spliced images. IJACSA) International Journal of Advanced Computer Science and Applications, 7(3), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070301.
  • 13. Tu Huynh-Kha, Thuong Le-Tien, Synh Ha-Viet-Uyen, Khoa Huynh-Van, and Marie Luong. A robust algorithm of forgery detection in copy-move and spliced images. IJACSA) International Journal of Advanced Computer Science and Applications, 7(3), 2016.
  • 14. R. Caldelli A. Del Bimbo G. Serra. I. Amerini, L. Ballan. A sift-based forensic method for copy-move attack detection and transformation recovery. pages pp. 1099–1110,. IEEE Transactions on Information Forensics and Security, vol. 6, issue 3, 2011. http://dx.doi.org/10.1109/TIFS.2011.2129512.
  • 15. Pravin Kakar and N Sudha. Exposing postprocessed copy–paste forgeries through transform-invariant features. IEEE Transactions on Information Forensics and Security, 7(3):1018–1028, 2012.
  • 16. Yongzhen Ke, Fan Qin, Weidong Min, and Guiling Zhang. Exposing image forgery by detecting consistency of shadow. The Scientific World Journal, 2014, 2014. http://dx.doi.org/10.1155/2014/364501.
  • 17. Shinfeng D Lin and Tszan Wu. An integrated technique for splicing and copy-move forgery image detection. In 2011 4th International Congress on Image and Signal Processing, volume 2, pages 1086–1090. IEEE, 2011. http://dx.doi.org/10.1109/CISP.2011.6100366.
  • 18. Shinfeng D Lin and Tszan Wu. An integrated technique for splicing and copy-move forgery image detection. In 2011 4th International Congress on Image and Signal Processing, volume 2, pages 1086–1090. IEEE, 2011.
  • 19. Tony Lindeberg. Scale invariant feature transform. 2012.
  • 20. Cecilia Pasquini, Carlo Brunetta, Andrea F Vinci, Valentina Conotter, and Giulia Boato. Towards the verification of image integrity in online news. pages 1–6, 2015. http://dx.doi.org/10.1109/ICMEW.2015.7169801.
  • 21. Christian Riess, Mathias Unberath, Farzad Naderi, Sven Pfaller, Marc Stamminger, and Elli Angelopoulou. Handling multiple materials for exposure of digital forgeries using 2-d lighting environments. Multimedia Tools and Applications, 76(4):4747–4764, 2017. http://dx.doi.org/10.1007/s11042-016-3655-0.
  • 22. 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, 8(8):1355–1370, 2013.
  • 23. M.A.-M. Salem. Multi-stage localization given topological map for autonomous robots. In International Conference on Computer Engineering and Systems, ICCES 2012, pages 55–60, 2012.
  • 24. Mohammed A.-M. Salem, Markus Appel, Frank Winkler, and Beate Meffert. Fpga-based smart camera for 3d wavelet-based image segmentation. In 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras, pages 1–8. IEEE, 2008.
  • 25. BL Shivakumar and S Santhosh Baboo. Detection of region duplication forgery in digital images using surf. International Journal of Computer Science Issues (IJCSI), 8(4):199, 2011.
  • 26. Ira Tuba, Eva Tuba, and Marko Beko. Digital image forgery detection based on shadow texture features. In 2016 24th Telecommunications Forum (TELFOR), pages 1–4. IEEE, 2016. http://dx.doi.org/10.1109/TELFOR.2016.7818875.
  • 27. Paul Viola and Michael J. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. Volume: 1, pp.511–518., pages 1257–1260. IEEE, 2009. http://dx.doi.org/10.1109/CVPR.2001.990517.
  • 28. Wei Wang, Jing Dong, and Tieniu Tan. Effective image splicing detection based on image chroma. In 2009 16th IEEE International Conference on Image Processing (ICIP), pages 1257–1260. IEEE, 2009. http://dx.doi.org/10.1109/ICIP.2009.5413549.
Uwagi
1. Track 2: Computer Science & Systems
2. Technical Session: 12th International Symposium on Multimedia Applications and Processing
3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4d0e2f1f-b5fe-415b-a054-6e7202a3bdf0
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