TY - JOUR
T1 - Deep learning for detection of object-based forgery in advanced video
AU - Yao, Ye
AU - Shi, Yunqing
AU - Weng, Shaowei
AU - Guan, Bo
N1 - Funding Information:
Acknowledgments: This work was supported in part by the Public Technology Application Research Project of ZheJiang Province under Grant 2017C33146, in part by the Humanities and Social Sciences Foundation of Ministry of Education of China under Grant 17YJC870021, in part by the National Natural Science Foundation of China under Grant 61571139.
Publisher Copyright:
© 2018 by the authors.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Convolutional neural network
KW - Deep learning approach
KW - Forgery detection and temporal localization
KW - Video object forgery detection
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U2 - 10.3390/sym10010003
DO - 10.3390/sym10010003
M3 - Article
AN - SCOPUS:85040864288
SN - 2073-8994
VL - 10
JO - Symmetry
JF - Symmetry
IS - 1
M1 - 3
ER -