Home // SECURWARE 2023, The Seventeenth International Conference on Emerging Security Information, Systems and Technologies // View article
Authors:
Dennis Siegel
Christian Kraetzer
Jana Dittmann
Keywords: forensics, media forensics, DeepFake detection, machine learning
Abstract:
DeepFake technology poses a new challenge to the validation of digital media integrity and authenticity. In contrast to ‘traditional’ forensic sub-disciplines (e.g., dactyloscopy), there are no standardized process models for DeepFake detection yet that would enable its usage in court in most countries. In this work, two existing best-practice methodologies (a data-centric model and a set of image authentication procedures) are combined and extended for the application of DeepFake detection. The extension includes aspects required to expand the focus from digital images to videos and enhancements in the quality assurance for methods (here focusing on the peer review aspect). The new methodology is applied to the example of DeepFake detection, utilizing three existing tools as methods. One for the Auxiliary data analysis and two DeepFake detectors based on hand-crafted and deep learning based feature spaces for Media content analysis are used. A total of 27 features were considered. In addition, the value types, ranges and their tendency for a DeepFake are determined for each feature. With the discussed potential extensions towards video evidence and machine learning, we identified additional requirements. These requirements are addressed in this paper as a proposal for an extended methodology to serve as starting point for future research and discussion in this domain.
Pages: 43 to 51
Copyright: Copyright (c) IARIA, 2023
Publication date: September 25, 2023
Published in: conference
ISSN: 2162-2116
ISBN: 978-1-68558-092-6
Location: Porto, Portugal
Dates: from September 25, 2023 to September 29, 2023