A Comprehensive Survey on Deep Learning Techniques for Digital Video Forensics

T. Vigneshwaran () and B. L. Velammal ()
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T. Vigneshwaran: Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore Kengeri Campus, Karnataka 560074, India
B. L. Velammal: Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai 600025, India

Journal of Information & Knowledge Management (JIKM), 2024, vol. 23, issue 03, 1-20

Abstract: With the help of advancements in connected technologies, social media and networking have made a wide open platform to share information via audio, video, text, etc. Due to the invention of smartphones, video contents are being manipulated day-by-day. Videos contain sensitive or personal information which are forged for one’s own self pleasures or threatening for money. Video falsification identification plays a most prominent role in case of digital forensics. This paper aims to provide a comprehensive survey on various problems in video falsification, deep learning models utilised for detecting the forgery. This survey provides a deep understanding of various algorithms implemented by various authors and their advantages, limitations thereby providing an insight for future researchers.

Keywords: Video falsification; digital forensics; social media networking; deep learning; convolutional neural networks; artificial intelligence; keyframe extraction; feature selection; classifier; object tracking (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219649224500345

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