Copy-Move Forgery Detection Using Fast Retina Keypoint (FREAK) Descriptor

Ghasemi Yegane, Ardeshir; Kiani, Kourosh; Rastgoo, Razieh

doi:10.22075/mseee.2023.24878.1075

Copy-Move Forgery Detection Using Fast Retina Keypoint (FREAK) Descriptor

Document Type : Research Article

Authors

Ardeshir Ghasemi Yegane

kourosh Kiani

Razieh Rastgoo

Faculty of Electrical and Computer Engineering (ECE), Semnan University,Semnan,Iran

10.22075/mseee.2023.24878.1075

Abstract

Image forgery, the manipulation of an image to hide some meaningful or helpful information, is widely used to manage the large amount of information being exchanged in the form of images. There are different forms of image forgery, and copy--move forgery is the most common form of it. The copy-move forgery is easy to perform yet challenging to detect due to the similarity between the original part of the image and the copied part. In this paper, we employ a keypoint descriptor inspired by the human visual system, namely the FREAK (Fast Retina Keypoint) descriptor, for robust copy-move forgery detection. This method uses the advantages of FREAK descriptor such as fast computing and low memory load compared to SIFT, SURF, and BRISK. Finally, geometric transformation parameters are extracted and discussed. Results confirm promising results in the case of image post-processing operations such as adding noise, illumination change, and geometric transformations such as rotation and scaling.

Keywords

Copy-move forgery detection

Fast Retina Keypoint (FREAK)

Keypoint descriptor

Subjects

Computer

[1] Diwan, A., Sharma, R., Roy, A.K., Mitra, S.K. (2021). Keypoint-based comprehensive copy-move forgery detection. IET Image Process 15, pp. 1298–1309.
[2] Gan, Y., Zhong, J., Vong, Ch. (2022). A Novel Copy-Move Forgery Detection Algorithm via Feature Label Matching and Hierarchical Segmentation Filtering. Information Processing & Management 59, No. 102783.
[3] Li, F.F. (2022). Machine Learning in Computer Vision, https://www.cs.princeton.edu.
[4] Ouyang, J., Liu, Y., Liao, M. (2017). Copy-move forgery detection based on deep learning. 2017 10th International Congress on Image and Signal Processing, Bio-Medical Engineering and Informatics (CISP-BMEI), Shanghai, China.
[5] Rodriguez-Ortega, Y., Ballesteros, D.M., Renza, D. (2021). Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics, J Imaging. Vol. 7, no. 3.
[6] Liu, Y., Xia, Ch., Zhu, X., Xu, Sh. (2020). Two-Stage Copy-Move Forgery Detection with Self Deep Matching and Proposal Superglue, arXiv: 2012.08697.
[7] Zainal Abidin, A.B., Abdul Majid, H.B., Samah, A.B.A., Hashim, H.B. (2019). Copy-Move Image Forgery Detection Using Deep Learning Methods: A Review, 2019 6th International Conference on Research and Innovation in Information Systems (ICRIIS), Johor Bahru, Malaysia.
[8] Kiani, K., Rezaeerad, S., Rastgoo, R. (2021). HMM-based Face Recognition Using SVD and Half of the Face Image, Journal of Modeling & Simulation in Electrical & Electronics Engineering (MSEEE), vol. 1, no. 2.
[9] Majidi, N., Kiani, K., Rastgoo, R. (2020). A deep model for super-resolution enhancement from a single image, Journal of AI and Data Mining, vol. 8, no. 4, pp. 451-460.
[10] Youssef, B., Atta, E. (2016). Image Forgery Detection using FREAK Binary Descriptor and Level Set Segmentation. International Journal of Scientific & Engineering Research 7.
[11] Sridevi, M., Aishwarya, S., Bokadia, D. (2019). Parallel Image Forgery Detection Using FREAK Descriptor. Information and Communication Technology for Intelligent Systems 107, pp. 619-630.
[12] Khudhair, Z.N., Mohamed, F., Kadhim, K.A. (2021). A Review on Copy-Move Image Forgery Detection Techniques. Journal of Physics: Conference Series.
[13] Alahi, A., Ortiz, R. & Vandergheynst, P. (2012). FREAK: Fast Retina Keypoint. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 510–517.
[14] Warif, N et al. (2016). Copy-move forgery detection: Survey, challenges, and future directions. Journal of Network and Computer Applications, vol. 75, pp. 259–278.
[15] Birajdar, G.K. & Mankar, V.H. (2013). Digital image forgery detection using passive techniques: A survey. Digital Investigation, Vol. 10, no. 3, pp. 226-245.
[16] Sridevi M., Mala C. & Sanyam S. (2012). Comparative Study of Image Forgery and Copy-Move Techniques. In: Wyld D., Zizka J., Nagamalai D. (eds). Advances in Computer Science, Engineering & Applications. Advances in Intelligent and Soft Computing, vol. 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30157-5_71.
[17] Meena, K.B. & Tyagi, V. (2019) Image Forgery Detection: Survey and Future Directions. In: Shukla R., Agrawal J., Sharma S., Singh Tomer G. (eds) Data, Engineering, and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-13-6351-1_14.
[18] Al_Azrak, F.M., Sedik, A., Dessowky, M.I. et al. (2020). An efficient method for image forgery detection based on trigonometric transforms and deep learning. Multimed Tools Appl, vol. 79, pp. 18221–18243. https://doi.org/10.1007/s11042-019-08162-3.
[19] Fridrich, J., Soukal, D. & Lukas, J. (2003). Detection of copy-move forgery in the digital images. Proceeding of DFRWS, Cleveland, OH.
[20] Farid, H. (2003). A picture tells a thousand lies. New Scientist, 6 Sep 2003.
[21] Popescu, A. & Farid, H. (2005). Exposing Digital Forgeries in Color Filter Array Interpolated Images. IEEE Trans. on signal processing, vol. 53, no. 10, pp. 3948-3959.
[22] Popescu, A. & Farid, H. (2004). Exposing digital forgeries by detecting duplicated image regions. Dartmouth College, Computer Science, Tech. Rep. TR2004-515.
[23] Luo, W., Huang, J. & Qiu, G. (2006). Robust detection of region-duplication forgery in digital image. Proc. of ICPR, Washington, USA.
[24] Li, G., Wu, Q., Tu, D. & Sun, S.J. (2007). A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. Proc. of IEEE ICME, Beijing, China.
[25] Mahdian, B & Saic, S. (2007). Detection of copy-move forgery using a method based on blur moment invariants. Forensic Science International, vol. 171, no. 2-3, pp. 180–189.
[26] Ryu, S.J., Lee, M.-J., Lee, H.-K. (2010). Detection of copy-rotate-move forgery using Zernike moments. Proc. of International Workshop on Information Hiding, Calgary, Canada.
[27] Bravo, S. & Nandi, A.K. (2011). Automated detection and localization of duplicated regions affected by reﬂection, rotation, and scaling in image forensics. Signal Processing, vol. 91, pp. 1759–70.
[28] Lowe. (1999). Object recognition from local scale-invariant features., 1999. The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157.
[29] Bay, H., Tuytelaars, T. & Van Gool. T. (2006). SURF: Speeded up robust features. ECCV, Graz, Austria, pp. 404–417.
[30] Leutenegger, S., Chli, M. & Siegwart, R. (2011). Brisk: Binary robust invariant scalable keypoints. 2011 International Conference on Computer Vision, Barcelona, Spain.
[31] Calonder, M., Lepetit, V., Strecha, C. & Fua, P. (2010). Brief: Binary robust independent elementary features. ECCV, Heraklion, Crete, Greece, pp. 778–792.
[32] Huang, H., Guo, W. & Zhang, Y. (2008). Detection of copy-move forgery in digital images using SIFT algorithm. Proc. of IEEE Paciﬁc-Asia Workshop on Computational Intell. and Industrial Application, Wuhan, China.
[33] Irene, L., Caldelli, R., Del, A. & Serra, G. (2011). A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans Inf Forensics Security, vol. 6, pp. 1099–110.
[34] Harris, C. & Stephens, M. (1988). A combined corner and edge detector. In Alvey vision conference, Manchester, UK, vol. 15, pp. 147-151.
[35] Rosten, E. & Drummond, T. (2006). Machine learning for high-speed corner detection. ECCV, Graz, Austria, vol. 1, pp. 430–443.
[36] Fischler, M. & Bolles, R. (1981). Random sample consensus: a paradigm for model ﬁtting with applications to image analysis and automated cartography. Communications of the ACM, vol. 24, no. 6, pp. 381–395.