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Article type: Research Article
Authors: Pandey, Rakshaa; * | Kushwaha, Alok Kumar Singha | Kumar, Vinayb
Affiliations: [a] School of Studies (Engineering and Technology), GGV, Chattisgarh, India | [b] FoET, University of Lucknow, Lucknow, India
Correspondence: [*] Corresponding author. Raksha Pandey, School of Studies (Engineering and Technology), GGV, Chattisgarh, India. E-mail: rakshasharma10@gmail.com.
Abstract: Video forgery, a prevalent concern in today’s digital age, involves the deliberate manipulation of video content, often carried out using sophisticated video editing software. In response to this challenge, the need for an automated approach to detect forged video footage has become increasingly pressing. Our proposed methodology addresses this need by employing a multi-faceted strategy. It begins with the classification of video frames as either originating from genuine sources or having undergone manipulation. To assess the authenticity, the Δ r¯s metric is applied to evaluate the coherence of frame sequences. Additionally, we’ve harnessed the power of machine learning, training a model on a diverse dataset, namely the VIFFD dataset. This robust machine learning approach, particularly the suggested Support Vector Machine (SVM) method, consistently achieves an impressive average accuracy of 94.4%, showcasing its potential as a dependable and effective solution for video forgery detection. In an era where the trustworthiness of video content is of paramount importance, our method emerges as a pivotal safeguard, contributing significantly to the preservation of the integrity and credibility of visual media.
Keywords: Correlation coefficient, forgery detection, interframe video forgery, machine learning, video forensic
DOI: 10.3233/JIFS-235818
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6807-6820, 2024
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