Digital video forgery detection based on statistical features calculation

Author(s):  
Andrey Kuznetsov

-We are living in the era of multimedia technology. Digital video occupies an imperative role in our daily life. With the use of omnipresent multimedia technology, we can create process, transmit and store digital information in many forms such as an image, audio and video. Digital video is convenient tool in forensic investigation, medical treatment, education, entertainment and other disparate fields. Videos are recorded by the people with their smart phones, camcorders, digital cameras and CCTV cameras. We have seen the rapid growth and development in the use of surveillance cameras. Videos recorded using these electronic and smart gadgets mostly contain crucial proof of most of the events. Inasmuch, the most affected to inter frame forgery which can be freely done by replication, insertion, removal and deletion of frames. However, the advancement and usage of inexpensive and effortless video editor software there has been tremendous growth in the consequences and risks of usage such editing techniques. Therefore, forgery is a technique of getting altered, fake and duplicate videos by joining, altering new video. Hence, the genuineness of such digital videos questionable and requires to be verified. In this paper review various video forgery detection methods those are applied to detect whether the video is original or duplicate, real or fake and digital video authentication techniques.


2017 ◽  
Vol 25 ◽  
pp. 4558-4574 ◽  
Author(s):  
Işılay BOZKURT ◽  
Mustafa Hakan BOZKURT ◽  
Güzin ULUTAŞ

2020 ◽  
Vol 12 (1) ◽  
pp. 14-34
Author(s):  
Chee Cheun Huang ◽  
Chien Eao Lee ◽  
Vrizlynn L. L. Thing

Video forgery has been increasing over the years due to the wide accessibility of sophisticated video editing software. A highly accurate and automated video forgery detection system will therefore be vitally important in ensuring the authenticity of forensic video evidences. This article proposes a novel Triangular Polarity Feature Classification (TPFC) video forgery detection framework for video frame insertion and deletion forgeries. The TPFC framework has high precision and recall rates with a simple and threshold-less algorithm designed for real-world applications. System robustness evaluations based on cross validation and different database recording conditions were also performed and validated. Evaluation on the performance of the TPFC framework demonstrated the efficacy of the proposed framework by achieving a recall rate of up to 98.26% and precision rate of up to 95.76%, as well as high localization accuracy on detected forged videos. The TPFC framework is further demonstrated to be capable of outperforming other modern video forgery detection techniques available today.


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