Interframe Forgery Video Detection: Datasets, Methods, Challenges, and Search Directions

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Udgivet i:Electronics vol. 14, no. 13 (2025), p. 2680-2722
Hovedforfatter: Ali, Mona M
Andre forfattere: Ghali, Neveen I, Hamza, Hanaa M, Hosny, Khalid M, Vrochidou Eleni, Papakostas, George A
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MDPI AG
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024 7 |a 10.3390/electronics14132680  |2 doi 
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100 1 |a Ali, Mona M  |u Department of Digital Media Technology, Faculty of Computers and Information, Future University in Egypt (FUE), New Cairo 11835, Egypt; mona.almakhton@fue.edu.eg (M.M.A.); neveen.ghali@fue.edu.eg (N.I.G.) 
245 1 |a Interframe Forgery Video Detection: Datasets, Methods, Challenges, and Search Directions 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a The authenticity of digital video content has become a critical issue in multimedia security due to the significant rise in video editing and manipulation in recent years. The detection of interframe forgeries is essential for identifying manipulations, including frame duplication, deletion, and insertion. These are popular techniques for altering video footage without leaving visible visual evidence. This study provides a detailed review of various methods for detecting video forgery, with a primary focus on interframe forgery techniques. The article evaluates approaches by assessing key performance measures. According to a statistical overview, machine learning has traditionally been used more frequently, but deep learning techniques are gaining popularity due to their outstanding performance in handling complex tasks and robust post-processing capabilities. The study highlights the significance of interframe forgery detection for forensic analysis, surveillance, and content moderation, as demonstrated through both evaluation and case studies. It aims to summarize existing studies and identify limitations to guide future research towards more robust, scalable, and generalizable methods, such as the development of benchmark datasets that reflect real-world video manipulation diversity. This emphasizes the necessity of creating large public datasets of manipulated high-resolution videos to support reliable integrity evaluations in dealing with widespread media manipulation. 
653 |a Forgery 
653 |a Machine learning 
653 |a Datasets 
653 |a Deep learning 
653 |a Forensic sciences 
653 |a Video recordings 
653 |a Robustness 
653 |a Task complexity 
653 |a Editing 
653 |a Business metrics 
700 1 |a Ghali, Neveen I  |u Department of Digital Media Technology, Faculty of Computers and Information, Future University in Egypt (FUE), New Cairo 11835, Egypt; mona.almakhton@fue.edu.eg (M.M.A.); neveen.ghali@fue.edu.eg (N.I.G.) 
700 1 |a Hamza, Hanaa M  |u Department of Information Technology, Faculty of Computers and Information, Zagazig University, Zagazig 44519, Egypt; hmkamal@fci.zu.edu.eg (H.M.H.); k_hosny@zu.edu.eg (K.M.H.) 
700 1 |a Hosny, Khalid M  |u Department of Information Technology, Faculty of Computers and Information, Zagazig University, Zagazig 44519, Egypt; hmkamal@fci.zu.edu.eg (H.M.H.); k_hosny@zu.edu.eg (K.M.H.) 
700 1 |a Vrochidou Eleni  |u MLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, Greece; gpapak@cs.duth.gr 
700 1 |a Papakostas, George A  |u MLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, Greece; gpapak@cs.duth.gr 
773 0 |t Electronics  |g vol. 14, no. 13 (2025), p. 2680-2722 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3229143788/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3229143788/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3229143788/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch