Abstract
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.
Highlights
Millions of images and videos are created and distributed through the Internet every day
We propose a novel video frame identification algorithm for object-based video tampering detection
We introduced an object-based video-tampering detection network using symmetrically overlapped motion residual features
Summary
Millions of images and videos are created and distributed through the Internet every day. Videos are often used after undergoing an editing process rather than being used as is. Because most of the editing is of good quality, there may be times when the reliability of the video is questionable. When videos used as evidence in courts or as news to report facts by the media are tampered with for malicious purposes, the consequences often cause serious social problems or harm. For this reason, video forgery detection has emerged as an important topic [1,2]
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