Detection and localization of inter-frame forgeries in videos based on macroblock variation and motion vector analysis. (January 2021)

• Record Type: Journal Article
• Authors: Bakas, Jamimamul; Naskar, Ruchira; Bakshi, Sambit
• Is Part Of: Computers & electrical engineering. Volume 89(2021)
• Publication Date: 2021-01
• Subjects: Digital forensics -- Inter-frame forgery -- Prediction footprint variation -- Variation of Motion Vectors -- Video forgery; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106929
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Surveillance videos and footages are the primary sources of evidence for any event or crime in the court of law. However, with the rapid advent of low-cost, computationally cheap video manipulating software and tools, video manipulation has become a no-brainer task today. This introduces a major challenge in authenticating the sanctity/originality of videos before they can be produced in the court, or used in other sensitive application domains. In this paper, we propose a digital forensic technique to detect inter-frame forgeries in surveillance videos. The proposed technique utilizes compressed domain video footprints i.e, prediction footprint variation and variation of motion vectors in videos, for the purpose of video forgery detection and localization. Through this work, we identify the type of forgery that has taken place in a video. We have performed experiment over 43 authentic and 720 inter-frame forged videos. Our experimental results indicate that the proposed technique performs consistently efficiently, irrespective of the group of pictures length and degree of compression in videos. Graphical abstract: Highlights: Provides a method for detection of inter-frame forgeries using compression features. Analyses intra-macroblocks of P-frames in a video to detect the outliers. Optimizes false positives in outlier detection exploiting motion vectors. Detects frame deletion, duplication and insertion types of inter-frame forgeries. Presents the experimental results: Performance improves with improve in video quality.