Xuan Hau Nguyen; Yongjian Hu; Muhmmad Ahmad Amin; Khan Gohar Hayat; Van Thinh Le; Dinh Tu Truong

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

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Author(s)

Xuan Hau Nguyen ^1,2,* Yongjian Hu ¹ Muhmmad Ahmad Amin ¹ Khan Gohar Hayat ¹ Van Thinh Le ² Dinh Tu Truong ³

1. School of Electronics and Information Engineering, South China University of Technology, Guangzhou 510640, P.R.China

2. Faculty Electronics of and Informatics Engineering Mien Trung Industrial and Trade College, Phu Yen 620000, Vietnam

3. Natural Language Processing and Knowledge Discovery Laboratory, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, 700000, Vietnam

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2020.03.01

Received: 3 Dec. 2019 / Revised: 8 Jan. 2020 / Accepted: 5 Feb. 2020 / Published: 8 Jun. 2020

Index Terms

Video forensic, video forgery detection, video inter-frame forgery detection, convolutional neural network, video authenticity, passive forensic

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 inter-frame 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.

Cite This Paper

Xuan Hau Nguyen, Yongjian Hu, Muhmmad Ahmad Amin, Khan Gohar Hayat, Van Thinh Le, Dinh-Tu Truong, " Detecting Video Inter-Frame Forgeries Based on Convolutional Neural Network Model", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.12, No.3, pp. 1-12, 2020. DOI: 10.5815/ijigsp.2020.03.01

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International Journal of Image, Graphics and Signal Processing (IJIGSP)