Passive Video Forgery Detection Considering Spatio-Temporal Consistency

  • Kazuhiro KonoEmail author
  • Takaaki Yoshida
  • Shoken Ohshiro
  • Noboru Babaguchi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 942)


This paper proposes a method for detecting forged objects in videos that include dynamic scenes such as dynamic background or non-stationary scenes. In order to adapt to dynamic scenes, we combine Convolutional Neural Network and Recurrent Neural Network. This enables us to consider spatio-temporal consistency of videos. We also construct new video forgery databases for object modification as well as object removal. Our proposed method using Convolutional Long Short-Term Memory achieved Area-Under-Curve (AUC) 0.977 and Equal-Error-Rate (EER) 0.061 on the object addition database. We also achieved AUC 0.872 and EER 0.219 on the object modification database.


Video forgery detection Dynamic scene Convolutional LSTM Modification database 



This work was supported by JSPS KAKENHI Grant Number JP16H06302.


  1. 1.
    Sowmya, K.N., Chennamma, H.R.: A survey on video forgery detection. Int. J. Comput. Eng. Appl. 9(2), 17–27 (2015)Google Scholar
  2. 2.
    Su, L., Huang, T., Yang, J.: A video forgery detection algorithm based on compressive sensing. Multimedia Tools Appl. 74(17), 6641–6656 (2015)CrossRefGoogle Scholar
  3. 3.
    Bestagini, P., Milani, S., Tagliasacchi, M., Tubaro, S.: Local tampering detection in video sequences. In: 2013 IEEE International Workshop on Multimedia Signal Processing, pp. 488–493 (2013)Google Scholar
  4. 4.
    Saxena, S., Subramanyam, A., Ravi, H.: Video inpainting detection and localization using inconsistencies in optical flow. In: 2016 IEEE Region 10 Conference, pp. 1361–1365 (2016)Google Scholar
  5. 5.
    Rota, P., Sangineto, E., Conotter, V., Pramerdorfer, C.: Bad teacher or unruly student: can deep learning say something in image forensics analysis? In: 23rd International Conference on Pattern Recognition, pp. 2503–2508 (2016)Google Scholar
  6. 6.
    Karita, S., Kono, K., Babaguchi, N.: Video forgery detection using a time series model in dynamic scenes. IEICE Technical Report, EMM2015-80, vol. 115, no. 479, pp. 25–30 (2016)Google Scholar
  7. 7.
    D’Avino, D., Cozzolino, D., Poggi, G., Verdoliva, L.: Autoencoder with recurrent neural networks for video forgery detection. Electron. Imaging 2017(7), 92–99 (2017)CrossRefGoogle Scholar
  8. 8.
    Newson, A., Almansa, A., Fradet, M., Gousseau, Y., P’erez, P.: Video inpainting of complex scenes. SIAM J. Imaging Sci. 7(4), 1993–2019 (2014)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Farid, H.: Image forgery detection. IEEE Signal Process. Mag. 26(2), 16–25 (2009)CrossRefGoogle Scholar
  10. 10.
    Cozzolino, D., Poggi, G., Verdoliva, L.: Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection. In: ACM Workshop on Information Hiding and Multimedia Security, pp. 159–164 (2017)Google Scholar
  11. 11.
    Richao, C., Gaobo, Y., Ningbo, Z.: Detection of object-based manipulation by the statistical features of object contour. Forensic Sci. Int. 236, 164–169 (2014)CrossRefGoogle Scholar
  12. 12.
    Lin, C.-S., Tsay, J.-J.: A passive approach for effective detection and localization of region-level video forgery with spatio-temporal coherence analysis. Digit. Investig. 11(2), 120–140 (2014)CrossRefGoogle Scholar
  13. 13.
    Yao, Y., Shi, Y., Weng, S., Guan, B.: Deep learning for detection of object based forgery in advanced video. Symmetry 10(3), 1–10 (2018)Google Scholar
  14. 14.
    Qadir, G., Yahaya, S., Ho, A.: Surrey university library for forensic analysis (SULFA) of video content. In: IET Conference on Image Processing, pp. 1–6 (2012)Google Scholar
  15. 15.
    Xingjian, S., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., Woo, W.-C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)Google Scholar
  16. 16.
    Bertalmio, M., Bertozzi, A.L., Sapiro, G.: Navier-stokes, fluid dynamics, and image and video inpainting. In: 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, no. C, pp. I.355–I.362 (2001)Google Scholar
  17. 17.
    Telea, A.: An image inpainting technique based on the fast marching method. J. Graph. Tools 9(1), 23–34 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Kazuhiro Kono
    • 1
    Email author
  • Takaaki Yoshida
    • 2
  • Shoken Ohshiro
    • 2
  • Noboru Babaguchi
    • 2
  1. 1.Faculty of Societal Safety SciencesKansai UniversityTakatsukiJapan
  2. 2.Graduate School of EngineeringOsaka UniversitySuitaJapan

Personalised recommendations