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Proceedings Paper

Using subject face brightness assessment to detect ‘deep fakes’ (Conference Presentation)
Author(s): Jeremy Straub

Paper Abstract

A technology to create videos that purport to be authentic recordings of a subject has been developed and, based on its use of deep learning neural networks, is commonly known as ‘deep fakes.’ The videos produced by this technology are visually indistinguishable from real recordings, in at least some cases. These videos can, prospectively, be used to defame celebrities, politicians and business leaders, to make false statements appear authentic and potentially even change the course of national and regional elections. Clearly, techniques are needed to ascertain the veracity of video recordings. Techniques have been proposed which measure blinking and other characteristics of the subject. However, these techniques which rely on minor flaws in the recreation video algorithm may disappear quickly, as developers improve their software. This paper presents work related to a search for a more systematic way to detect artificially constructed videos. Specifically, the work presented herein analyzes the brightness of the subject’s face, comparing algorithmically constructed / reconstructed videos to actual recordings made under similar circumstances. Brightness is compared and averaged across the image. Additionally, pixel-to-adjacent-pixels and regional differences are compared and contrasted. Comparative results for constructed and original recordings are presented. Also discussed are the differences in results from using different individuals as the basis for the subject’s reconstruction, including the subject him or herself. Based on the results presented and the analysis discussed, the paper discusses the pros and cons of using subject face brightness as a technique for detecting fakes, across several application areas.

Paper Details

Date Published: 14 May 2019
Proc. SPIE 10996, Real-Time Image Processing and Deep Learning 2019, 109960H (14 May 2019); doi: 10.1117/12.2520546
Show Author Affiliations
Jeremy Straub, North Dakota State Univ. (United States)

Published in SPIE Proceedings Vol. 10996:
Real-Time Image Processing and Deep Learning 2019
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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