Deep learning for detection of object-based forgery in advanced video

Document Type

Article

Publication Date

1-1-2018

Abstract

Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN) to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNNmodel. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results.

Identifier

85040864288 (Scopus)

Publication Title

Symmetry

External Full Text Location

https://doi.org/10.3390/sym10010003

e-ISSN

20738994

Issue

1

Volume

10

Grant

61571139

Fund Ref

National Natural Science Foundation of China

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