Research Article of American Journal of Computer Sciences and Applications
Detecting Video Inter-Frame Forgeries Based on Convolutional Neural Network Models
Xuan Hau Nguyena, Yongjian HUb, Khan Gohar Hayatc, Van Thinh Led, Tu D. Truonge
a,cResearch Centre of Multimedia Information Security Detection and Intelligent Processing, School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, P.R.China. a,dFaculty of Information Technology Central Industrial and Commercial College, Phu Yen 620000, VietNam, eFaculty of Information Technology Ton Duc Thang University, Ho Chi Minh 700000, VietNam
In the era of information explosion today, videos are easily captured and made viral in a short time and tampering videos have become easier due to editing software. So, the authenticity of videos become very essential. Video inter-frame forgeries are the most common type of video forgery methods. Until now some algorithms have been suggested for detecting inter-frame forgeries based on handicraft features but the accuracy and speed in processing of suggested algorithms are still challenging. In this paper, we are going to put forward a video forgeries detection method for detecting video inter-frame forgeries based on convolutional neural network (CNN) models by retraining the available CNN models trained on ImageNet dataset. The proposed methods based on CNN models which have been retrained to exploit spatial-temporal relationships in a video to robustly detect inter-frame forgeries. And in order to eliminate the errors due by the network, we have proposed a confidence score instead of the raw output score from networks. Through the results of experiments, we have proven that the proposed method has significantly higher efficiency and accuracy than recent methods.
Keywords: Video forensic, video forgery detection, video inter-frame forgery detection, convolutional neural network, video authenticity, passive forensic.
How to cite this article:
Xuan Hau Nguyen, Yongjian HU, Khan Gohar Hayat, Van Thinh Le, Tu D. Truong. Detecting Video Inter-Frame Forgeries Based on Convolutional Neural Network Models. American Journal of Computer Sciences and Applications, 2020; 3:25.
1. Milani, S., et al., An overview on video forensics. APSIPA Transactions on Signal and Information Processing, 2012. 1.
2. Yang, J., T. Huang, and L. Su, Using similarity analysis to detect frame duplication forgery in videos. Multimedia Tools and Applications, 2016. 75(4): p. 1793-1811.
3. Singh, G. and K. Singh, Video frame and region duplication forgery detection based on correlation coefficient and coefficient of variation. Multimedia Tools and Applications, 2018: p. 1-36.
4. Wang, Q., et al., Video inter-frame forgery identification based on consistency of correlation coefficients of gray values. Journal of Computer and Communications, 2014. 2(04): p. 51.
5. Subramanyam, A. and S. Emmanuel. Video forgery detection using HOG features and compression properties. in 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP). 2012. IEEE.
6. Kobayashi, M., T. Okabe, and Y. Sato, Detecting forgery from static-scene video based on inconsistency in noise level functions. IEEE Transactions on Information Forensics and Security, 2010. 5(4): p. 883-892.
7. Yu, L., et al., Exposing frame deletion by detecting abrupt changes in video streams. Neurocomputing, 2016. 205: p. 84-91.
8. He, K., et al. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
9. Huang, G., et al. Densely connected convolutional networks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
10. Chen, Y., et al. Dual path networks. in Advances in Neural Information Processing Systems. 2017.
11. Szegedy, C., et al. Going deeper with convolutions. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
12. Krizhevsky, A., I. Sutskever, and G.E. Hinton. Imagenet classification with deep convolutional neural networks. in Advances in neural information processing systems. 2012.
13. Szegedy, C., et al. Inception-v4, inception-resnet and the impact of residual connections on learning. in Thirty-First AAAI Conference on Artificial Intelligence. 2017.
14. Zoph, B. and Q.V. Le, Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578, 2016.
15. Sandler, M., et al. Mobilenetv2: Inverted residuals and linear bottlenecks. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
16. Deng, J., et al. Imagenet: A large-scale hierarchical image database. in 2009 IEEE conference on computer vision and pattern recognition. 2009. Ieee.
17. Wang, W. and H. Farid. Exposing digital forgeries in video by detecting duplication. in Proceedings of the 9th workshop on Multimedia & security. 2007. ACM.
18. Chao, J., X. Jiang, and T. Sun. A novel video inter-frame forgery model detection scheme based on optical flow consistency. in International Workshop on Digital Watermarking. 2012. Springer.
19. Liu, Y. and T. Huang, Exposing video inter-frame forgery by Zernike opponent chromaticity moments and coarseness analysis. Multimedia Systems, 2017. 23(2): p. 223-238.
20. Long, C., A. Basharat, and A. Hoogs, A Coarse-to-fine Deep Convolutional Neural Network Framework for Frame Duplication Detection and Localization in Video Forgery. arXiv preprint arXiv:1811.10762, 2018.
21. Zoph, B., et al. Learning transferable architectures for scalable image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
22. Oquab, M., et al. Learning and transferring mid-level image representations using convolutional neural networks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
23. Weiss, K., T.M. Khoshgoftaar, and D. Wang, A survey of transfer learning. Journal of Big data, 2016. 3(1): p. 9.
24. Chollet, F. Xception: Deep learning with depthwise separable convolutions. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
25. Simonyan, K. and A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
26. Horn, B.K. and B.G. Schunck, Determining optical flow. Artificial intelligence, 1981. 17(1-3): p. 185-203.
27. Jia, S., et al., Coarse-to-fine copy-move forgery detection for video forensics. IEEE Access, 2018. 6: p. 25323-25335.
28. Kingra, S., N. Aggarwal, and R.D. Singh, Inter-frame forgery detection in H. 264 videos using motion and brightness gradients. Multimedia Tools and Applications, 2017. 76(24): p. 25767-25786.
29. Al Hamidi, S., VFDD (Video Forgery Detection Database) Version 1.0. http://sites.scut.edu.cn/misip/main.psp, 2017.