Video forgery detection method based on local difference binary

Abstract

Recently, the rapid development of video editing software has made video forgery applicable. Researchers have proposed methods to detect forged video frames. These methods utilize codec properties, motion artifacts, noise effect and frame similarity to detect forgery. Execution time and low detection accuracy are the two main drawbacks of forgery detection methods reported in the literature. In this study, a new frame duplication detection method using Local Difference Binary (LDB) is proposed to extract features from the frames. Distance between similar frames that have similar feature vectors are is used by the method to estimate Distance of Forgery and to determine the exact location of duplicated frames. PSNR between similar frames are is then used to group them into three classes, and rule-based mechanism reports forged frames according to the membership to classes. Experimental results indicate that the proposed method has lower execution time with higher accuracy than similar works.

Keywords

• Video forgery
• LDB
• Frame duplication detection
• Distance of forgery

Kaynakça

1. [1] Wang W, Farid H. “Exposing digital forgeries in video by detecting double MPEG compression”. Proceedings of the 8th workshop on Multimedia Security Conference, Geneva, Switzerland, 26-27 September 2006.
2. [2] Wang W, Farid H. “Exposing digital forgeries in video by detecting duplication”. Proceedings of the 9th workshop on Multimedia Security Conference, Texas, USA, 20-21 September 2007.
3. [3] Wang W, Farid H. “Exposing digital forgeries in interlaced and deinterlaced video”. IEEE Transaction on Information. Forensics and Security, 2(3), 438-449, 2007.
4. [4] Luo W, Wu M, Huang J. “MPEG recompression detection based on block artifacts”. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference, San Jose, California, United States, 27-31 January 2008.
5. [5] Wang W, Farid H. “Exposing digital forgeries in video by detecting double quantization”. Proceedings of the 11th ACM Workshop on Multimedia and security Conference, Princeton, USA, 11-13 September 2009.
6. [6] Su Y, Zhang J, Liu J. “Exposing digital video forgery by detecting motion compensated edge artifact”. International Conference on Computational Intelligence and Software Engineering, Wuhan, China, 11-13 December 2009.
7. [7] Zhang J, Su Y, Zhang M. “Exposing digital video forgery by ghost shadow artifact”. Proceedings of the 1th ACM workshop on multimedia in forensics Conference, Beijing, China, 23 October 2009.
8. [8] Hu Y, Li CT, Wang Y, Liu BB. “An improved fingerprinting algorithm for detection of video frame duplication forgery”. International Journal of Digital Crime and Forensics, 4(3), 64-76, 2013.

9. [9] Lin GS, Chang JF, Chuang CH. “Detecting frame duplication based on spatial and temporal analysis”. 6th International Conference on Computer Science & Education Conference, Singapore, Singapore, 3-5 August 2011.
10. [10] Sun T, Wang W, Jiang X. “Exposing video forgeries by detecting double MPEG compression”. IEEE International Conference on Acoustics, Speech and Signal Processing, Kyoto, Japan, 25-30 March 2012.
11. [11] Subramanyam AV, Emmanuel S. “Video forgery detection using HOG features and compression properties”. IEEE 14th International Workshop on Multimedia Signal Processing (MMSP), Banff, AB, Canada, 17-19 September 2012.
12. [12] Chao J, Jiang X, Sun T. “A novel video inter-frame forgery model detection scheme based on optical flow consistency”. The International Workshop on Digital Forensics and Watermarking Conference, Shanghai, China, 31 October 2012.
13. [13] Lin GS, Chang JF. “Detection of frame duplication forgery in videos based on spatial and temporal analysis”. International Journal of Pattern Recognition and Artificial Intelligence, 26 (7), 1-18, 2013.
14. [14] Liao SY, Huang TQ. “Video copy move forgery detection and localization based on Tamura texture features”. 6th International Congress on Image and Signal Processing, Hangzhou, China, 16-18 December 2013.
15. [15] Lin CS, Tsay JJ. “A passive approach for effective detection and localization of region-level video forgery with spatio-temporal coherence analysis”. Digital Investigation, 11(2), 120-140, 2014.
16. [16] Su L, Huang T, Yang J. “A video forgery detection algorithm based on compressive sensing”. Multimedia Tools and Applications, 74(17), 6641-6656, 2015.
17. [17] Yang J, Huang T, Su L. “Using similarity analysis to detect frame duplication forgery in videos”. Multimedia Tools and Applications, 75(4), 1793-1811, 2016.
18. [18] Yang X, Cheng KTT. “Local difference binary for ultrafast and distinctive feature extraction”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(1), 188-194, 2014.
19. [19] Calonder M, Lepetit V, Strecha C, Fua P. “Brief: Binary Robust Independent Elementary Features”. 11th European Conference on Computer Vision, Heraklion, Greece, 5-11 September 2010.
20. [20] Rublee E, Rabaud V, Konolige K, Bradski G. “ORB: an Efficient Alternative to SIFT or SURF”. In Proceeding of ‘International Conference on Computer Vision (ICCV)’. Barcelona, Spain, 6-13 November 2011.
21. [21] Leutengger S, Chli M, Siegwart RY. “BRISK: Binary Robust Invariant Scalable Keypoints”. In Proceeding of ‘International Conference on Computer Vision (ICCV)’, Barcelona, Spain, 6-13 November 2011.
22. [22] Alahi A, Ortiz R, Vandergheynst P. “FREAK: Fast Retinal Keypoint”. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 16-21 June 2012.
23. [23] Singh G, Singh K. “Video frame and region duplication forgery detection based on correlation coefficient and coefficient of variation”. Multimedia Tools and Applications, 78(7), 11527-11562, 2019.
24. [24] Ulutas G, Ustubioglu B, Ulutas M, Nabiyev V. “Frame duplication/mirroring detection method with binary features”. IET Image Processing, 11(5), 333-342, 2017.
25. [25] Ulutas G, Ustubioglu, B, Ulutas M, Nabiyev V. “Frame duplication detection based on bow model”. Multimedia Systems, 24(5), 549-567, 2018.
26. [26] Yao Y, Shi Y, Weng S, Guan, B. “Deep learning for detection of object based forgery in advanced video”. Symmetry, 10(1), 1-10, 2018.
27. [27] Bakas J, Bashaboin A K, Naskar R. "MPEG Double Compression Based Intra-Frame Video Forgery Detection using CNN". 2018 International Conference on Information Technology (ICIT), Bhubaneswar, India, 10-12 January 2018.
28. [28] Long C, Basharat A, Hoogs A. “A coarse to fine deep convolutional neural network framework for frame duplication detection and localization in forged videos”. CVPR Workshops, Salt Lake City, Utah, 18-22 June 2018.
29. [29] Raveendra M, Nagireddy K. “DNN based moth search optimization for video forgery detection”. International Journal of Engineering and Advanced Technology, 9(1), 1190-1199, 2019.
30. [30] D’Avino D, Cozzolino D, Poggi G. “Autoencoder with recurrent neural networks for video forgery detection”. Electronic Imaging, 7(1), 92-99, 2017.