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Process-Driven Modelling of Media Forensic Investigations-Considerations on the Example of DeepFake Detection

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Název: Process-Driven Modelling of Media Forensic Investigations-Considerations on the Example of DeepFake Detection
Autoři: Christian Kraetzer, Dennis Siegel, Stefan Seidlitz, Jana Dittmann
Zdroj: Sensors, Vol 22, Iss 3137, p 3137 (2022)
Informace o vydavateli: MDPI AG
Rok vydání: 2022
Sbírka: Directory of Open Access Journals: DOAJ Articles
Témata: media forensics, forensic process model, certifiable investigation methods, DeepFake detection, Chemical technology, TP1-1185
Popis: Academic research in media forensics mainly focuses on methods for the detection of the traces or artefacts left by media manipulations in media objects. While the resulting detectors often achieve quite impressive detection performances, when tested under lab conditions, hardly any of those have yet come close to the ultimate benchmark for any forensic method, which would be courtroom readiness. This paper tries first to facilitate the different stakeholder perspectives in this field and then to partly address the apparent gap between the academic research community and the requirements imposed onto forensic practitioners. The intention is to facilitate the mutual understanding of these two classes of stakeholders and assist with first steps intended at closing this gap. To do so, first a concept for modelling media forensic investigation pipelines is derived from established guidelines. Then, the applicability of such modelling is illustrated on the example of a fusion-based media forensic investigation pipeline aimed at the detection of DeepFake videos using five exemplary detectors (hand-crafted, in one case neural network supported) and testing two different fusion operators. At the end of the paper, the benefits of such a planned realisation of AI-based investigation methods are discussed and generalising effects are mapped out.
Druh dokumentu: article in journal/newspaper
Jazyk: English
Relation: https://www.mdpi.com/1424-8220/22/9/3137; https://doaj.org/toc/1424-8220; https://doaj.org/article/1bedfd28f8694be6a6cf87fb533763ac
DOI: 10.3390/s22093137
Dostupnost: https://doi.org/10.3390/s22093137
https://doaj.org/article/1bedfd28f8694be6a6cf87fb533763ac
Přístupové číslo: edsbas.26CD48BA
Databáze: BASE
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  – Url: https://doi.org/10.3390/s22093137#
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  Data: Process-Driven Modelling of Media Forensic Investigations-Considerations on the Example of DeepFake Detection
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Christian+Kraetzer%22">Christian Kraetzer</searchLink><br /><searchLink fieldCode="AR" term="%22Dennis+Siegel%22">Dennis Siegel</searchLink><br /><searchLink fieldCode="AR" term="%22Stefan+Seidlitz%22">Stefan Seidlitz</searchLink><br /><searchLink fieldCode="AR" term="%22Jana+Dittmann%22">Jana Dittmann</searchLink>
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  Data: Sensors, Vol 22, Iss 3137, p 3137 (2022)
– Name: Publisher
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  Data: MDPI AG
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  Label: Publication Year
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  Data: 2022
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  Data: Directory of Open Access Journals: DOAJ Articles
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  Data: <searchLink fieldCode="DE" term="%22media+forensics%22">media forensics</searchLink><br /><searchLink fieldCode="DE" term="%22forensic+process+model%22">forensic process model</searchLink><br /><searchLink fieldCode="DE" term="%22certifiable+investigation+methods%22">certifiable investigation methods</searchLink><br /><searchLink fieldCode="DE" term="%22DeepFake+detection%22">DeepFake detection</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+technology%22">Chemical technology</searchLink><br /><searchLink fieldCode="DE" term="%22TP1-1185%22">TP1-1185</searchLink>
– Name: Abstract
  Label: Description
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  Data: Academic research in media forensics mainly focuses on methods for the detection of the traces or artefacts left by media manipulations in media objects. While the resulting detectors often achieve quite impressive detection performances, when tested under lab conditions, hardly any of those have yet come close to the ultimate benchmark for any forensic method, which would be courtroom readiness. This paper tries first to facilitate the different stakeholder perspectives in this field and then to partly address the apparent gap between the academic research community and the requirements imposed onto forensic practitioners. The intention is to facilitate the mutual understanding of these two classes of stakeholders and assist with first steps intended at closing this gap. To do so, first a concept for modelling media forensic investigation pipelines is derived from established guidelines. Then, the applicability of such modelling is illustrated on the example of a fusion-based media forensic investigation pipeline aimed at the detection of DeepFake videos using five exemplary detectors (hand-crafted, in one case neural network supported) and testing two different fusion operators. At the end of the paper, the benefits of such a planned realisation of AI-based investigation methods are discussed and generalising effects are mapped out.
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  Data: 10.3390/s22093137
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  Data: https://doi.org/10.3390/s22093137<br />https://doaj.org/article/1bedfd28f8694be6a6cf87fb533763ac
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