Detection of deleted frames on videos using a 3D convolutional neural network

V. Voronin; R. Sizyakin; A. Zelensky; A. Nadykto; I. Svirin

doi:10.1117/12.2326806

2 November 2018 Detection of deleted frames on videos using a 3D convolutional neural network

V. Voronin, R. Sizyakin, A. Zelensky, A. Nadykto, I. Svirin

Proceedings Volume 10802, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II; 108020U (2018) https://doi.org/10.1117/12.2326806
Event: SPIE Security + Defence, 2018, Berlin, Germany

Abstract

Digital video forgery or manipulation is a modification of the digital video for fabrication, which includes frame sequence manipulations such as deleting, insertion and swapping. In this paper, we focus on the detection problem of deleted frames in videos. Frame dropping is a type of video manipulation where consecutive frames are deleted to skip content from the original video. The automatic detection of deleted frames is a challenging task in digital video forensics. This paper describes an approach using spatial-temporal analysis based on the convolution with a bank of 3D Gabor filters. Also, we use the 3D Convolutional Neural Network for frame drop detection for preprocessed frames. Experimental results demonstrate the effectiveness of the proposed approach on a test video database.

Conference Presentation

Citation Download Citation

V. Voronin, R. Sizyakin, A. Zelensky, A. Nadykto, and I. Svirin "Detection of deleted frames on videos using a 3D convolutional neural network", Proc. SPIE 10802, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II, 108020U (2 November 2018); https://doi.org/10.1117/12.2326806

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