Detecting Video Inter-Frame Forgeries Based on Convolutional Neural Network Model

Abstract

In the era of information extension today, videos are easily captured and made viral in a short time, and video tampering has become more comfortable due to editing software. So, the authenticity of videos becomes more essential. Video inter-frame forgeries are the most common type of video forgery methods, which are difficult to detect by the naked eye. Until now, some algorithms have been suggested for detecting inter-frame forgeries based on handicraft features, but the accuracy and processing speed of those algorithms are still challenging. In this paper, we are going to put forward a video forgery detection method for detecting video interframe forgeries based on convolutional neural network (CNN) models by retraining the available CNN model trained on ImageNet dataset. The proposed method based on state-the-art CNN models, which are retrained to exploit spatial-temporal relationships in a video to detect inter-frame forgeries robustly and we have also proposed a confidence score instead of the raw output score based on these networks for increasing accuracy of the proposed method.Through the experiments, the detection accuracy of the proposed method is 99.17%. This result has shown that the proposed method has significantly higher efficiency and accuracy than other recent methods.

Video Forgery Detection using an Improved BAT with Stacked Auto Encoder Model

“…They utilized I3D and ResNet, achieving an accuracy of 84.05% on the VIRAT dataset and 85.88% on the MFC dataset. The research [12] introduced an approach for identifying inter-frame forgeries through Convolutional Neural Networks (CNN). This method involved the retraining of existing CNN models trained on the ImageNet dataset, enabling the detection of inter-frame forgeries while leveraging spatial-temporal relationships in a video.…”