SURF ve MSER kombinasyonu ile kopya taşı sahteciliği algılama

Year 2022, Volume: 11 Issue: 3, 513 - 521, 18.07.2022

Yıldız Çiltaş Funda Akar

https://doi.org/10.28948/ngumuh.1075784

Abstract

Sayısal görüntüler çeşitli veriler içerebildiğinden bilgi paylaşımı için önemli bir kaynak olarak kabul edilmektedir. Ayrıca, görüntüler gerçek hayatta birçok vakada kanıt olarak yaygın olarak kullanılmaktadır. Dijital fotoğrafların popülaritesindeki hızlı artış, teknolojilerin gelişmesinden kaynaklanmaktadır. Dijital görüntüleri değiştirmek için Photoshop ve Corel Photo gibi son yıllarda çeşitli yazılım programları geliştirilmiştir, bu programlar sahtecilik için de yaygın olarak kullanılmaktadır. Teknolojik gelişmeler nedeniyle, insanların sahte görüntüleri çıplak gözle tanıması zordur. Bu nedenle, bu çalışmada, tespit edilmesi zor olan sahte görüntülerin doğru etiketlenmesini sağlamak için sahtecilik tespit problemlerinde sık kullanılan öznitelikler birleştirilmiştir. Hızlandırılmış Sağlam Öznitelikler (SURF) ve Maksimum Kararlı Ekstremal Bölgeler (MSER) birleştirilerek daha güçlü öznitelik elde edilmiştir. Deneysel sonuçlara bakıldığında; kopyala-taşı sahtecilik tespit problemlerinde iki yöntemin birleştirmesi sonucu elde edilen önerilen yöntemin kullanılmasının SURF ve MSER özniteliklerinin ayrı ayrı kullanılması durumuna göre daha başarılı olduğu gözlemlenmiştir.

Keywords

Kopyala-Taşı Sahtecilik Tespiti, SURF, MSER, Görüntü Sahteciliği

Thanks

Çalışmamızın değerlendirme sürecinde verdiği emeklerden dolayı Sayın Editör ve Editör kuruluna teşekkür ederiz.

Copy move forgery detection with SURF and MSER combination

Abstract

Because digital images may contain a variety of data, they are regarded as an important source for information sharing. Also, images are widely used as evidence in a variety of real-life cases. The rapid rise in popularity of digital photographs is due to the improvement of technologies. Several software programs have been developed in recent years to modify digital images, such as Photoshop and Corel Photo, however these programs are now being used extensively for forgery. Because of technological advancements, it is difficult for people to recognize faked images with their naked eyes Therefore, in this study, the features used in forgery detection problems are combined to ensure accurate labeling of even forgery images that are difficult to detect. Stronger feature is obtained by combining Speeded-Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER). Considering the experimental results; it has been observed that the use of the proposed method, which is obtained as a result of combining the two methods in copy-move forgery detection problems, is more successful than using the SURF and MSER features separately.

Keywords

Copy-Move Forgery Detection, SURF, MSER, Image Forensics

References

• M. Hassaballah, A. A. Abdelmgeid, and H. A. Alshazly, Image Features Detection, Description and Matching, Image Feature Detectors and Descriptors : Foundations and Applications, A. I. Awad and M. Hassaballah, Eds. Cham: Springer International Publishing, 11–45, 2016.
• G. Ulutas and G. Muzaffer, A New Copy Move Forgery Detection Method Resistant to Object Removal with Uniform Background Forgery, Mathematical Problems in Engineering, 2016, https://doi.org/10.1155/2016/ 3215162.
• D. Ozdemir and D. Celik, Analysis of Encrypted Image Data with Deep Learning Models, 14th International Conference on Information Security and Cryptology, ISCTURKEY Proceedings, 121–126, 2021. https://doi .org/10.1109/ISCTURKEY53027.2021.9626.
• M. A. Qureshi and M. Deriche, A bibliography of pixel-based blind image forgery detection techniques, Signal Processing: Image Communication, (39), 46–74, 2015, https://doi.org/10.1016/j.image.2015.08.008.
• M. Kashif, T. M. Deserno, D. Haak, and S. Jonas, Feature description with SIFT, SURF, BRIEF, BRISK, or FREAK A general question answered for bone age assessment, Computers in Biology and Medicine, (68), November, 67–75, 2016, https://doi.org/10.1016/j.com pbiomed.2015.11.006.
• K. Asghar, Z. Habib, and M. Hussain, Copy-move and splicing image forgery detection and localization techniques: a review, Australian Journal of Forensic Sciences, 49, (3), 281–307, 2017, https://doi.org/10.10 8/00450618.2016.1153711.
• T. Mahmood, T. Nawaz, A. Irtaza, R. Ashraf, M. Shah, and M. T. Mahmood, Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images, Mathematical Problems in Engineering, 2016, https://doi.org/10.1155/2016/8713202.
• O. I. Al-Sanjary and G. Sulong, Detection of video forgery: A review of literature, Journal of Theoretical and Applied Information Technology, 74, (2), 207–220, 2015.
• N. P. Joglekar and P. N. Chatur, A Compressive Survey on Active and Passive Methods for Image Forgery Detection, International Journal Of Engineering And Computer Science, 4, (1), 10187–10190, 2015.
• R. Oommen, M. Jayamohan, and S. Sruthy, A Survey of Copy-Move Forgery Detection Techniques for Digital Images, International Journal of innovations in engineering and technology, 5, (2), 419–426, 2015.
• J. A. Redi, W. Taktak, and J. L. Dugelay, Digital image forensics: A booklet for beginners, Multimedia Tools and Applications, 51, (1), 133–162, 2011, https://doi.or g/10.1007/s11042-010-0620-1.
• B. L. Shivakumar and S. S. Baboo, Detecting Copy-Move Forgery in Digital Images: A Survey and Analysis of Current Methods, Global Journal of Computer Science and Technology, 10, (7), 61–65, 2011.
• Z. Zhang, C. Wang, and X. Zhou, A survey on passive image copy-move forgery detection, Journal of Information Processing Systems, 14, (1), 6–31, 2018, https://doi.org/10.3745/JIPS.02.0078.
• P. C. Sekhar and T. Shankar, Review on Image Splicing Forgery Detection, International Journal of Computer Science and Information Security, 14, (11), 471–475, 2016.
• R. Raj and N. Joseph, Keypoint Extraction Using SURF Algorithm for CMFD, Procedia Computer Science, (93), 375–381, 2016, https://doi. org/10.1016/ j.procs.2016.07.223.
• V. Christlein, C. Riess, J. Jordan, C. Riess, and E. Angelopoulou, An evaluation of popular copy-move forgery detection approaches, IEEE Transactions on Information Forensics and Security, 7, (6), 1841–1854, 2012, https://doi.org/10.1109/TIFS.2012.2218597.
• M. Hassaballah and A. I. Awad, Detection and Description of Image Features: An Introduction, Image Feature Detectors and Descriptors : Foundations and Applications, A. I. Awad and M. Hassaballah, Eds. Cham: Springer International Publishing, 1–8, 2016.
• K. Mikolajczyk et al., A comparison of affine region detectors, International Journal of Computer Vision, 65, (1–2), 43–72, 2005, https://doi.org/10.1007/s11263 -005-3848-x.
• G. J. Burghouts and J.-M. Geusebroek, Performance evaluation of local colour invariants, Computer Vision and Image Understanding, 113, (1), 48–62, 2009, https://doi.org/10.1016/j.cviu.2008.07.003.
• I. Abu Doush and S. AL-Btoush, Currency recognition using a smartphone: Comparison between color SIFT and gray scale SIFT algorithms, Journal of King Saud University - Computer and Information Sciences, 29, (4), 484–492, 2017, https://doi.org/10.1016/j.jksuci.20 16.06.003.
• E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, ORB: An efficient alternative to SIFT or SURF, Proceedings of the IEEE International Conference on Computer Vision, pp. 2564–2571, 2011, https://doi.org /10.1109/ICCV.2011.6126544.
• D. G. Lowe, Object recognition from local scale-invariant features, Proceedings of the IEEE International Conference on Computer Vision, 2, pp. 1150–1157, 1999, https://doi.org/10.1109/ICCV.1999. 790410.
• H. Bay, T. Tuytelaars, and L. Van Gool, LNCS 3951- SURF: Speeded Up Robust Features, Computer Vision–ECCV, pp. 404–417, 2006, [Online]. Avai lable: https://link.springer.com/chapter/10.1007/11744 023_32.
• J. Matas, O. Chum, M. Urban, and T. Pajdla, Robust wide-baseline stereo from maximally stable extremal regions, Image and Vision Computing, 22, (10) SPEC. ISS., 761–767, 2004, https://doi.org/10.1016/j.imavis. 2004.02.006 .
• F. Akar and Y. Aydın, Comparison of Interest Point-Based Features in Object Recognition Applications, 8th International Advanced Technologies Symposium (IATS'17), Elazığ, Türkiye, 19-22, pp. 3553-3556, 2017.
• K. Ramirez-Gutierrez, Mariko-Nakano, G. Sanchez-Perez, and H. Perez-Meana, Copy-move forgery detection algorithm using frequency transforms, surf and mser, 2019 7th International Workshop on Biometrics and Forensics, IWBF, pp. 4–9, 2019, doi:1 0.1109/IWBF.2019.8739168.
• K. Ramirez-Gutierrez, M. Nakano-Miyatake, G. Sanchez-Perez, Blind Tamper Detection to Copy Move Image Forgery using SURF and MSER, MMEDIA, 9, 2015.
• B. Soni and P. K. Das, Geometric Transformation Invariant Improved Block-Based Copy-Move Forgery Detection, in Image Copy-Move Forgery Detection : New Tools and Techniques, Singapore: Springer Singapore, 51–67, 2022.
• M. Bansal, M. Kumar, and M. Kumar, 2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors, Multimedia Tools and Applications, 80, (12), 18839–18857, 2021, https:// doi.org/10.1007/s11042-021-10646-0.
• C. Lin, W. Lu, et al., Copy-move forgery detection using combined features and transitive matching, Multimedia Tools and Applications, 78, (21), 30081–30096, 2019, https://doi.org/10.1007/s11042-018-6922-4.
• K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, (10), 1615–1630, 2005, https://doi.org/10.1109/TPAMI.20 05.188.
• H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding, 110, (3), 346–359, 2008, https://doi.org/10.1016/j.cviu.2007.09.014.
• M. A. Fischler and R. C. Bolles, Random sample consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography, Communications of the ACM, 24, (6), 381–395, 1981, https://doi.org/10.1145/358669.35869 2.
• D. Tralic, I. Zupancic, S. Grgic, and M. Grgic, CoMoFoD - New database for copy-move forgery detection, Proceedings Elmar - International Sympo sium Electronics in Marine, pp. 49–54, 2013.
• V. T. Manu and B. M. Mehtre, Copy-move tampering detection using affine transformation property preservation on clustered keypoints, Signal, Image and Video Processing, 12, (3), 549–556, 2018, https://doi .org/10.1007/s11760-017-1191-7.
• M. Bilal, H. A. Habib, Z. Mehmood, T. Saba, and M. Rashid, Single and Multiple Copy–Move Forgery Detection and Localization in Digital Images Based on the Sparsely Encoded Distinctive Features and DBSCAN Clustering, Arabian Journal for Science and Engineering, 45, (4), 2975–2992, 2020, https://doi.org/ 10.1007/s13369-019-04238-2.
• A. Kumar, A. Bhavsar, and R. Verma, Syn2Real: Forgery Classification via Unsupervised Domain Adaptation, Proceedings-2020 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW, 63–70, 2020, https://doi .org/10 .1109/WACVW50321.2020.9096921
• B. Xu, J. Wang, G. Liu, and Y. Dai, Image copy-move forgery detection based on SURF, Proceedings - 2010 2nd International Conference on Multimedia Information Networking and Security, MINES 2010, pp. 889–892, 2010, https://doi.org/10.1109/MINES.20 10. 189.
• Kanica Sachdev, A Novel Technique for Detection of Copy Move Forgery Using MSER Features, International Journal of Emerging Technologies in Engineering Research (IJETER), 5, (9), 14–19, 2017, [Online]. Available: https://ijeter.everscie nce.org/Ma nuscripts/Volume-5/Issue-9/Vol-5-issue-9-M-03.pdf.
• D. Cozzolino, G. Poggi, and L. Verdoliva, Efficient Dense-Field Copy-Move Forgery Detection, IEEE Transactions on Information Forensics and Security, 10, (11), 2284–2297, 2015, https://doi.org/10.1109/TIF S.2015.2 455334.