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Content Provider | MDPI |
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Author | Yao, Ye Shi, Yunqing Weng, Shaowei Guan, Bo |
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 CNN model. 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. |
File Size | 308224 |
File Format | |
e-ISSN | 20738994 |
DOI | 10.3390/sym10010003 |
Journal | Symmetry |
Issue Number | 1 |
Volume Number | 10 |
Language | English |
Publisher Date | 2017-12-26 |
Access Restriction | Open |
Subject Keyword | deep learning approach convolutional neural network video object forgery detection forgery detection and temporal localization |
Content Type | Text |
Resource Type | Article |
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