Multidimensional Systems and Signal Processing

, Volume 29, Issue 3, pp 1173–1190 | Cite as

A novel passive forgery detection algorithm for video region duplication

  • Lichao Su
  • Cuihua Li


Forgery involving region duplication is one of the most common types of video tampering. However, few algorithms have been suggested for detecting this type of forgery effectively, especially for videos to which a mirroring operation was applied. In this paper, we summarize the properties of duplication forgery of video regions and propose a novel algorithm to detect this forgery. First, the algorithm extracts the feature points in the current frame. The tampered areas in the current frame are then searched, which is implemented in three steps. Finally, our algorithm detects the tampered areas in the remaining frames using spatio-temporal context learning and outputs the detection results. The experimental results demonstrate the satisfactory performance of our algorithm for detecting videos subjected to mirror operations and its higher efficiency than previous algorithms.


Video forgery Region duplication Mirror invariant Passive forensics 



This work was supported by National Natural Science Foundation of China under Grant Number 61373077, the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20110121110020) and the National Defense Basic Scientific Research Program of China.


  1. Al-Qershi, O. M., & Khoo, B. E. (2013). Passive detection of copy-move forgery in digital images: State-of-the-art. Forensic Science International, 231(1), 284–295.CrossRefGoogle Scholar
  2. Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., & Serra, G. (2011). A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Transactions on Information Forensics and Security, 6(3), 1099–1110.CrossRefGoogle Scholar
  3. Cao, Y., Gao, T., Fan, L., & Yang, Q. (2012). A robust detection algorithm for copy-move forgery in digital images. Forensic Science International, 214(1), 33–43.CrossRefGoogle Scholar
  4. Chen, W., & Shi, Y. (2009). Detection of double mpeg compression based on first digit statistics. Lecture notes in computer science, Digital Watermarking, 5450, 16–30.Google Scholar
  5. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005. 2005. IEEE (pp. 886–893).Google Scholar
  6. Gao, B., & Jin, Y. (2010). Detection of Image copy-move tamper Using SURF in digital forensics. In: 2010 Asia-Pacific conference on information network and digital content security (pp. 58–62).Google Scholar
  7. Hsu, C.-C., Hung, T.-Y., Lin, C.-W., & Hsu, C.-T. (2008). Video forgery detection using correlation of noise residue. In: 2008 IEEE 10th Workshop on Multimedia Signal Processing, 2008 (pp. 170–174). IEEE.Google Scholar
  8. Jolliffe, I. (2002). Principal component analysis. New York: Wiley.zbMATHGoogle Scholar
  9. Kakar, P., & Sudha, N. (2012). Exposing postprocessed copy-paste forgeries through transform-invariant features. IEEE Transactions on Information Forensics and Security, 7(3), 1018–1028.CrossRefGoogle Scholar
  10. Kobayashi, M., Okabe, T., & Sato, Y. (2009). Detecting video forgeries based on noise characteristics. In: Advances in image and video technology (pp. 306–317). Berlin: Springer.Google Scholar
  11. Li, W., Yuan, Y., & Yu, N. (2009). Passive detection of doctored JPEG image via block artifact grid extraction. Signal Processing, 89(9), 1821–1829.CrossRefzbMATHGoogle Scholar
  12. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.CrossRefGoogle Scholar
  13. Ma, R., Chen, J., & Su, Z. (2010). MI-SIFT: Mirror and inversion invariant generalization for SIFT descriptor. In: Proceedings of the ACM international conference on image and video retrieval, ACM (pp. 228–235).Google Scholar
  14. Milani, S., Fontani, M., Bestagini, P., Barni, M., Piva, A., Tagliasacchi, M., et al. (2012). An overview on video forensics. APSIPA Transactions on Signal and Information Processing, 1, e2.CrossRefGoogle Scholar
  15. Oppenheim, A. V., Willsky, A. S., & Nawab, S. H. (1983). Signals and systems, vol. 2. Englewood Cliffs, NJ: Prentice-Hall. 6(7):10.Google Scholar
  16. Pun, C.-M., Yuan, X.-C., & Bi, X.-L. (2015). Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Transactions on Information Forensics and Security, 10(8), 1705–1716.CrossRefGoogle Scholar
  17. Qadir, G., Yahaya, S., & Ho, A. T. (2012). Surrey university library for forensic analysis (SULFA) of video content. In: IET conference on image processing (IPR 2012). IET (pp. 1–6).Google Scholar
  18. Sencar, H. T., & Memon, N. (2008). Overview of state-of-the-art in digital image forensics. Algorithms, Architectures and Information Systems Security, 3, 325–348.Google Scholar
  19. Subramanyam, A., & Emmanuel, S. (2012). Video forgery detection using HOG features and compression properties. In: 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP), 2012. IEEE (pp. 89–94).Google Scholar
  20. Torralba, A. (2003). Contextual priming for object detection. International Journal of Computer Vision, 53(2), 169–191.MathSciNetCrossRefGoogle Scholar
  21. Wang, W., & Farid, H. (2007). Exposing digital forgeries in video by detecting duplication. In: Proceedings of the 9th workshop on multimedia and security, ACM (pp. 35–42).Google Scholar
  22. Wang, W., & Farid, H. (2009). Exposing digital forgeries in video by detecting double quantization. In: Proceedings of the 11th ACM workshop on multimedia and security, 2009 (pp. 39–48). ACM.Google Scholar
  23. Yang, J., Huang, T., & Su, L. (2014). Using similarity analysis to detect frame duplication forgery in videos. Multimedia Tools and Applications, 1–19.Google Scholar
  24. Yin, H., Hui, W., Li, H., Lin, C., & Zhu, W. (2012). A novel large-scale digital forensics service platform for Internet videos. IEEE Transactions on Multimedia, 14(1), 178–186.CrossRefGoogle Scholar
  25. Zhang, K., Zhang, L., Liu, Q., Zhang, D., & Yang, M.-H. (2014). Fast visual tracking via dense spatio-temporal context learning. In: Computer vision–ECCV 2014 (pp. 127–141). Springer.Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Information Science and EngineeringXiamen UniversityFujianChina

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