License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.13.1.1
URN: urn:nbn:de:0030-drops-191177
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/19117/
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Amerini, Irene ; Rocha, Anderson ; Rosin, Paul L. ; Sun, Xianfang
Weitere Beteiligte (Hrsg. etc.): Irene Amerini and Anderson Rocha and Paul L. Rosin and Xianfang Sun

Media Forensics and the Challenge of Big Data (Dagstuhl Seminar 23021)

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dagrep_v013_i001_p001_23021.pdf (2 MB)

Abstract

With demanding and sophisticated crimes and terrorist threats becoming more pervasive, allied with the advent and widespread of fake news, it becomes paramount to design and develop objective and scientific-based criteria to identify the characteristics of investigated materials associated with potential criminal activities. We need effective approaches to help us answer the four most important questions in forensics regarding an event: "who," "in what circumstances," "why," and "how." In recent years, the rise of social media has resulted in a flood of media content. As well as providing a challenge due to the increase in data that needs fact-checking, it also allows leveraging big-data techniques for forensic analysis.
The seminar included sessions on traditional, deep learning-based methods, big data, benchmark and performance evaluation, applications, and future directions. It aimed to orchestrate the research community’s efforts in such a way that we harness different tools to fight misinformation and the spread of fake content.

BibTeX - Entry

@Article{amerini_et_al:DagRep.13.1.1,
  author =	{Amerini, Irene and Rocha, Anderson and Rosin, Paul L. and Sun, Xianfang},
  title =	{{Media Forensics and the Challenge of Big Data (Dagstuhl Seminar 23021)}},
  pages =	{1--35},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{13},
  number =	{1},
  editor =	{Amerini, Irene and Rocha, Anderson and Rosin, Paul L. and Sun, Xianfang},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/19117},
  URN =		{urn:nbn:de:0030-drops-191177},
  doi =		{10.4230/DagRep.13.1.1},
  annote =	{Keywords: Digital forensics, Image and video authentication, Image and video forensics, Image and video forgery detection, Tampering detection}
}

Keywords: Digital forensics, Image and video authentication, Image and video forensics, Image and video forgery detection, Tampering detection
Collection: DagRep, Volume 13, Issue 1
Issue Date: 2023
Date of publication: 18.09.2023

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