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A trust-based, multi-factor system for assessing the veracity of video files in the era of ‘deep fakes’ (Conference Presentation)
Author(s): Jeremy Straub
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Paper Abstract

A technology for creating video files that purport to be of an individual speaking words he or she has never necessarily said has been developed. This technology, commonly known as ‘deep fakes,’ uses machine learning technologies to train a system with images of the prospective subject of a video and reconstruct a video based upon words spoken by another individual. As these videos are convincing and can have negative effects ranging from embarrassing a subject to interfering with elections to impacting national security, it is critical to identify ways to determine whether a prospective video is genuine or not. This paper proposes a system to evaluate a presented video file, based on multiple characteristics, and make a recommendation as to the confidence of the veracity of the file. From a technical perspective, it combines assessment of multiple details related to the audio and video stored in the file, as well as other file characteristics. Additionally, other inputs related to assessment of the content of the video, its impact and timing can be added to this technical confidence metric to have a combined single metric that characterizes trust in the video to support decision making related to it. Several examples are presented and assessed. The paper concludes with a discussion of the problems of nefarious fake videos, the impact of fakeness detection and future work in this area. In particular, the impact of identifying a video as a fake towards countering the impact of the nefarious video is considered.

Paper Details

Date Published: 13 May 2019
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Proc. SPIE 10993, Mobile Multimedia/Image Processing, Security, and Applications 2019, 109930G (13 May 2019); doi: 10.1117/12.2520510
Show Author Affiliations
Jeremy Straub, North Dakota State Univ. (United States)


Published in SPIE Proceedings Vol. 10993:
Mobile Multimedia/Image Processing, Security, and Applications 2019
Sos S. Agaian; Vijayan K. Asari; Stephen P. DelMarco, Editor(s)

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