Determination of the Optimal Threshold Value and Number of Keypoints in Scale Invariant Feature Transform-based Copy-Move Forgery Detection

R Rizal Isnanto, Ajub Ajulian Zahra, Imam Santoso, Muhammad Salman Lubis

Abstract


The copy-move forgery detection (CMFD) begins with the preprocessing until the image is ready to process. Then, the image features are extracted using a feature-transform-based extraction called the scale-invariant feature transform (SIFT). The last step is features matching using Generalized 2 Nearest-Neighbor (G2NN) method with threshold values variation. The problem is what is the optimal threshold value and number of keypoints so that copy-move detection has the highest accuracy. The optimal threshold value and number of keypoints had determined so that the detection has the highest accuracy. The research was carried out on images without noise and with Gaussian noise.

Full Text:

PDF

References


G. Palmer, “A Road Map for Digital Forensic Research,” Technical Report (DTR-T001-01) for Digital Forensic Research Workshop, New York, 2001.

M. Puri and V. Chopra, “A Survey: Copy-Move Forgery Detection Methods.” International Journal of Computer Systems (IJCS), vol. 3, no. 9, pp: 582-586, September 2016.

S. Khan, and A. Kulkarni, “An Efficient Method for Detection of Copy-Move Forgery Using Discrete Wavelet Transform. International Journal on Computer Science and Engineering,” Vol. 02, No. 05, 2010, pp. 1801-1806.

J. Fridrich, D. Soukal, and J. Lukas, “Detection of Copy-Move Forgery in Digital Images,” Proceedings of Digital Forensic Research Workshop, IEEE Computer Society, August 2003, pp. 55–61.

P. Popescu and H. Farid, “Exposing Digital Forgeries by Detecting Duplicated Image Regions,” Computer Science, Technical Report (TR2004-515), Dartmouth College, 2004.

W. Luo and J. Huang, “Robust Detection of Region-Duplication Forgery in Digital Image,” IEEE - The 18th International Conference on Pattern Recognition (ICPR'06), 2006.

D.G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, no. 2, January 2004, pp. 91-110.

B. Mahdian and S. Saic, “Detection of Copy-Move Forgery using a Method based on Blur Moment Invariants,” Forensic Science International, an international journal dedicated to the applications of medicine and science in the administration of justice, vol.171, no. 2-3, September 2007, pp. 181-189.

I. Amerini, L. Ballan, R. Caldelli, A.D. Bimbo, and G. Serra, “A SIFT-based Forensic Method for Copy-Move Attack Detection and Transformation Recovery,” IEEE Transactions on Information Forensics and Security, vol. 6, no. 3, September 2011, doi 10.1109/TIFS.2011.2129512, pp. 1099-1110

L. Li, S. Li, and H. Zhu, “An Efficient Scheme for Detecting Copy-Move Forged Images by Local Binary Patterns,” Journal of Information Hiding and Multimedia Signal Processing, vol. 4, no. 1, January 2013, pp. 46-56.

V. Jabade, and S. Gengaje, “Modelling of Geometric Attacks for Digital Image Watermarking,” IJIERT - International Journal of Innovations in Engineering Research and Technology, vol. 3, no. 3, March 2016.

M.B. Ranjani, and R. Poovendran, “Image Duplication Copy-Move Forgery Detection Using Discrete Cosine Transforms Method,” International Journal of Applied Engineering Research, vol. 11, no. 4, 2016, pp. 2671-2674.

M. Osamah, A. Al-Qershi and K.B. Ee, "Passive Detection of Copy-Move Forgery in Digital Images: State-of-the-Art," Forensic Science International, vol. 231, no. 1, September 2013, pp. 284-295.

P. Mukherjee, S. Mitra, “A Review on Copy-Move Forgery Detection Techniques Based on DCT and DWT,” International Journal of Computer Science and Mobile Computing IJCSMC, vol. 4, no. 3, March 2015, pp.702 – 708.

E. Ardizzone, A. Bruno, and G. Mazzola, “Copy-Move Forgery Detection by Matching Triangles of Keypoints,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 10, October 2015, pp. 2084 – 2094.

K. Sharma, P. Abrol, and Devanand, “D. Feature Based Analysis of Copy-Paste Image Tampering Detection,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), vol. 2, no. 6, 2017, pp. 555-562.

S. Wenchang, Z. Fei, Q. Bo, and L. Bin, “Improving Image Copy-Move Forgery Detection with Particle Swarm Optimization Techniques,” China Communications, vol. 13, no. 1, January 2016, pp. 139 – 149.

Y.D. Shin, “Fast Exploration of Copy-Move Forgery Image,” Advanced Science and Technology Letters, vol. 123, 2016, pp.1-5.

V. Christlein and J. Jordan, “An Evaluation of Popular Copy-Move Forgery Detection Approaches,” IEEE Transactions on Information Forensics and Security, 2012, pp. 1-26.

G. Ulutas, and M. Ulutas, “Image Forgery Detection using Color Coherence Vector,” Electronics, Computer and Computation (ICECCO), November 2013, pp. 107-110.

M.A. Farooque and J.S. Rohankar, “Survey on Various Noises and Techniques for Denoising the Color Image,” International Journal of Application or Innovation in Engineering & Management (IJAIEM), vol. 2, no. 11, November 2013.

D. Chauhana, D. Kasatb, S. Jainc, and V. Thakared, ”Survey on Keypoint Based Copy-Move Forgery Detection Methods on Image,” Elsevier-International Conference on Computational Modeling and Security (CMS 2016), pp. 206 – 212.

C.M. Pun, X.C. Yuanand, and X.L. Bi, “Image Forgery Detection Using Adaptive Over-Segmentation and Feature Point Matching,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 8, August 2015, pp. 1705 – 1716.

G. Kaur and M. Dutta, “Digital Image Forgery: A Survey,” International Journal of Computer Science Research and Technology (IJCSRT), vol. 1, no. 6, November 2013, pp.1-7.

B. Ustubioglu, G. Ulutas, M. Ulutas, and V.V. Nabiyev, “A New Copy-Move Forgery Detection Technique with Automatic Threshold Determination,” Elsevier - International Journal of Electronics and Communications, vol. 70, no. 8, August 2016, pp. 1076–1087.

F.C. Huang, S.Y. Huang, J.W. Ker, and Y.C. Chen, “High-performance SIFT Hardware Accelerator for Real-time Image Feature Extraction,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 3, March 2012, pp. 340-351.

C. Wang, Z. Zhang, and X. Zhou, “An Image Copy-Move Forgery Detection Scheme Based on A-KAZE and SURF Features,” Symmetry 2018, 10, 706, doi:10.3390/sym10120706, Switzerland, 2018, pp. 1-20.

C.S. Prakash, P.P. Panzade, H. Om, and S. Maheshkar, “Detection of Copy-Move Forgery using AKAZE and SIFT Keypoint Extraction,” Multimedia Tools and Applications, August 2019, vol. 78, no. 16, pp 23535–23558.

M.A. Elaskily, H.K. Aslan, M.M. Dessouky, F.E. Abd El-Samie, O.S. Faragallah, and O.A. Elshakankiry, “Enhanced Filter-based SIFT Approach for Copy-Move Forgery Detection,” Menoufia Journal of Electronic Engineering Research (MJEER), vol. 28, no. 1, January 2019, pp. 159-181.

Y. Wu, W. Abd-Almageed, and P. Natarajan, “BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization,” Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 168-184.

L. D’Amiano, D. Cozzolino, G. Poggi, and L. Verdoliva, “A Patchmatch-based Dense-field algorithm for video copy–move detection and localization,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 3, 2018, pp. 669-682.

D. Cozzolino, G. Poggi, and L. Verdoliva, “Copy-move Forgery Detection based on Patchmatch,” 2014 IEEE International Conference on Image Processing (ICIP), October 2014, pp. 5312-5316.

Y. Li, “Image Copy-move Forgery Detection based on Polar Cosine Transform and Approximate Nearest Neighbor Searching,” Forensic Science International, vol. 1, no. 1-3, 2013, pp. 59-67.

D. Cozzolino, G. Poggi, and L. Verdoliva, “Efficient Dense-field Copy–move Forgery Detection,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 11, 2015, 2284-2297.

S. Bayram, H.T. Sencar, and N. Memon, “An Efficient and Robust Method for Detecting Copy-move Forgery,” 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, April 2009, pp. 1053-1056.

F. Marra, D. Gragnaniello, L. Verdoliva, and G. Poggi, “A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection,” September 2019, arXiv preprint arXiv:1909.06751.

J. Li, X. Li, B. Yang, and X. Sun, “Segmentation-based Image Copy-move Forgery Detection Scheme,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 3, 2014, pp. 507-518.


Refbacks

  • There are currently no refbacks.


International Journal of Electronics and Telecommunications
is a periodical of Electronics and Telecommunications Committee
of Polish Academy of Sciences

eISSN: 2300-1933