Video Forensics for Object Removal Based on Two-Channel Convolutional Neural Network

In order to deal with the problem that the existing video object removal forensics methods had low recognition accuracy of the forged frame, a video forensics algorithm based on two-channel convolutional neural network was proposed in this paper. This method used a two-channel structure to extract the RGB feature and noise feature of the absolute frame difference image respectively, and the bilinear pooling was utilized to perform feature fusion on the two features. The classification result of the video frames was output through the classification layer, and this method could then identify the forged frame effectively. The RGB channel was able to find the unnatural tampering boundary and contrast in the absolute frame difference image, and the noise channel was able to find the inconsistency of noise between the original area and the tampered. In addition, the algorithm added a preprocessing layer to amplify the forged traces of tampered video frame. The experimental results revealed that the proposed detection algorithm effectively improves the recognition accuracy of forged video frame, and compared with the traditional single-channel network structure, the pattern of two-channel feature fusion achieved better detection performance.