Video tampering localisation using features learned from authentic content.

Johnston, Pamela; Elyan, Eyad; Jayne, Chrisina

Abstract

Video tampering detection remains an open problem in the field of digital media forensics. As video manipulation techniques advance, it becomes easier for tamperers to create convincing forgeries that can fool human eyes. Deep learning methods have already shown great promise in discovering effective features from data, particularly in the image domain, however they are exceptionally data hungry. Labelled datasets of varied, state-of-the-art, tampered video which are large enough to facilitate machine learning do not exist and, moreover, may never exist while the field of digital video manipulation is advancing at such an unprecedented pace. Therefore, it is vital to develop techniques which can be trained on authentic or synthesised video but used to localise the patterns of manipulation within tampered videos. In this paper, we developed a framework for tampering detection which derives features from authentic content and utilises them to localise key frames and tampered regions in three publicly available tampered video datasets. We used Convolutional Neural Networks (CNNs) to estimate quantisation parameter, deblock setting and intra/inter mode of pixel patches from an H.264/AVC sequence. Extensive evaluation suggests that these features can be used to aid localisation of tampered regions within video.

Citation

JOHNSTON, P., ELYAN, E. and JAYNE, C. 2020. Video tampering localisation using features learned from authentic content. Neural computing and applications [online], 32(16): special issue on Real-world optimization problems and meta-heuristics and selected papers from the 19th Engineering applications of neural networks conference 2018 (EANN 2018), 3-5 September 2018, Bristol UK , pages 12243-12257. Available from: https://doi.org/10.1007/s00521-019-04272-z

• Journal Article Type: Article
• Conference Name: 19th Engineering applications of neural networks conference 2018 (EANN 2018)
• Acceptance Date: May 21, 2019
• Online Publication Date: May 30, 2019
• Publication Date: Aug 31, 2020
• Deposit Date: May 24, 2019
• Publicly Available Date: May 24, 2019
• Journal: Neural computing and applications
• Print ISSN: 0941-0643
• Electronic ISSN: 1433-3058
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 32
• Issue: 16
• Pages: 12243-12257
• DOI: https://doi.org/10.1007/s00521-019-04272-z
• Keywords: CNN; Compression; Video tampering detection; Deep learning
• Public URL: https://rgu-repository.worktribe.com/output/243607
• Contract Date: May 24, 2019