Frame Identification of Object-Based Video Tampering Using Symmetrically Overlapped Motion Residual

Image and video manipulation has been actively used in recent years with the development of multimedia editing technologies. However, object-based video tampering, which adds or removes objects within a video frame, is posing challenges because it is difficult to verify the authenticity of videos. In this paper, we present a novel object-based frame identification network. The proposed method uses symmetrically overlapped motion residuals to enhance the discernment of video frames. Since the proposed motion residual features are generated on the basis of overlapped temporal windows, temporal variations in the video sequence can be exploited in the deep neural network. In addition, this paper introduces an asymmetric network structure for training and testing a single basic convolutional neural network. In the training process, two networks with an identical structure are used, each of which has a different input pair. In the testing step, two types of testing methods corresponding to two- and three-class frame identifications are proposed. We compare the identification accuracy of the proposed method with that of the existing methods. The experimental results demonstrate that the proposed method generates reasonable identification results for both two- and three-class forged frame identifications.