Yongjian Hu

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

E-mail: eeyjhu@scut.edu.cn


Research Interests: Information Security, Multimedia Information System, Data Structures and Algorithms, Information-Theoretic Security


Yongjian Hu was born in Wuhan, Hubei, graduated from Xi'an Jiaotong University in 1990 with a master's degree in information and control engineering. Yongjian Hu received the Ph.D. degree in communication and information systems from South China University of Technology in 2002. Now he works as full Professor in School of Electronic and Information Engineering at South China University of Technology. From 2011 to 2013, he worked as Marie Curie Fellow in the Department of Computer Science, University of Warwick, UK. From 2006 to 2008, he worked as Research Professor in the Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST), South Korea. From 2005 to 2006, he worked as Research Professor in the School of Information and Communication Engineering, SungKyunKwan University, South Korea. Between 2000 and 2004, he visited the Department of Computer Science, City University of Hong Kong four times as a research assistant, senior research associate, and research fellow, respectively. Dr. Hu is Senior Member of IEEE. He is also Senior Member of Chinese Institute of Electronics (CIE) and Senior Member of China Computer Federation (CCF). He has published more than 70 peer reviewed papers. His research interests include information hiding, multimedia security and machine learning

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

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

DOI: https://doi.org/10.5815/ijigsp.2020.03.01, Pub. Date: 8 Jun. 2020

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.

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Three-dimensional Region Forgery Detection and Localization in Videos

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

DOI: https://doi.org/10.5815/ijigsp.2019.12.01, Pub. Date: 8 Dec. 2019

Nowadays, with the extensive use of cameras in many areas of life, every day millions of videos are uploaded on the internet. In addition, with rapidly developing video editing software applications, it has become easier to forge any video. These software applications have made it challenging to detect forged videos, especially with forged videos have duplication of three-dimensional (3-D) regions. Recently, there has been increased interest in detecting forged videos, but there are very limited studies to detect forged videos which were duplicated 3-D regions. So, our research focused on this weakness and proposed a new method, which can be used for detecting and locating 3-D duplicated regions in videos based on the phase-correlation of 3-D regions residual more efficiently. To evaluate the efficiency of the proposed method, we experimented with two realistic datasets VFDD-3D and REWIND-3D. The results of the experiments proved that the proposed method is efficient and robust for detecting small 3-D regions duplication and frame sequences duplication, especially localization of duplication forgery in videos has shown impressive results.

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