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.
Highlights
Due to the rapid development of digital video technology, editing or tampering a video sequence becomes much easier than before
The main contributions are described as follows: (1) We propose a convolutional neural network (CNN)-based model with five layers to automatically learn high-dimension features from the input image patches; (2) Different from the traditional CNN models in the field of computer vision, we let video frames go through three preprocessing layers before feeding our CNN model
An absolute difference algorithm is applied to the input video sequence, the output difference frames are clipped to image patches by means of asymmetric data augmentation strategy
Summary
Due to the rapid development of digital video technology, editing or tampering a video sequence becomes much easier than before. More and more researchers focus on the study of video tampering detection. Object forgery detection has become a new topic in the research field of digital video passive forensics [2]. Object forgery in video is a common video tampering method by means of adding new objects to a video sequence or removing existing ones [3,4]. In contrast to the image copy-move forensics approaches [5,6,7], video object tamper detection is a more challenging task. If we use image forensics algorithms to detect video tampering, the computational cost will be unacceptable. The temporal correlation between video frames should be considered to reduce the complexity of video forensics
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