Video Forgery

2009 ◽  
pp. 7-11
Author(s):  
Timothy K. Shih ◽  
Joseph C. Tsai ◽  
Nick C. Tang ◽  
Shih-Ming Chang ◽  
Yaurice Y. Lin
Keyword(s):  
Video Forgery

A Comprehensive Analysis on Video Forgery Detection Techniques

2020 ◽  
Author(s):  
Vinay Kumar ◽  
Abhishek Singh ◽  
Vineet kansal ◽  
Manish Gaur
Keyword(s):  
Comprehensive Analysis ◽  
Forgery Detection ◽  
Detection Techniques ◽  
Video Forgery Detection ◽  
Video Forgery

Video tampering dataset development in temporal domain for video forgery authentication

2020 ◽  
Vol 79 (33-34) ◽  
pp. 24553-24577
Author(s):  
Hitesh D. Panchal ◽  
Hitesh B. Shah
Keyword(s):  
Temporal Domain ◽  
Video Forgery

scholarly journals A new video forgery detection approach based on forgery line

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

scholarly journals A Novel Video Forgery Detection Model Based on Triangular Polarity Feature Classification

2020 ◽  
Vol 12 (1) ◽  
pp. 14-34
Author(s):  
Chee Cheun Huang ◽  
Chien Eao Lee ◽  
Vrizlynn L. L. Thing
Keyword(s):  
Detection System ◽  
Recall Rate ◽  
Video Frame ◽  
Forgery Detection ◽  
Feature Classification ◽  
Localization Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Video Forgery Detection ◽  
Video Forgery

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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