DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection

David Fischinger; Martin Boyer

DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection

David Fischinger, Martin Boyer

Data Science & Artifical Intelligence

Publikation: Beitrag in Buch oder Tagungsband › Vortrag mit Beitrag in Tagungsband › Begutachtung

27 Downloads (Pure)

Abstract

The deliberate manipulation of public opinion, especially through altered images, which are frequently disseminated through online social networks, poses a significant danger to society. To fight this issue on a technical level we support the research community by releasing the Digital Forensics 2023 (DF2023) training and validation dataset, comprising one million images from four major forgery categories: splicing, copy-move, enhancement and removal. This dataset enables objective comparison of network architectures and significantly reduces researchers’ time effort for preparing datasets.

Originalsprache	Englisch
Titel	DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection
Redakteure/-innen	Peter Corcoran
Erscheinungsort	https://iprcs.github.io/pdf/IMVIP2023_Proceeding.pdf
Seiten	128-135
Seitenumfang	8
Publikationsstatus	Veröffentlicht - 2023
Veranstaltung	25th Irish Machine Vision and Image Processing Conference - Galway, Ireland, Galway, Irland Dauer: 30 Aug. 2023 → 1 Sept. 2023 https://www.universityofgalway.ie/c3i/news-&-events/imvip_2023/

Konferenz

Konferenz	25th Irish Machine Vision and Image Processing Conference
Kurztitel	IMVIP 2023
Land/Gebiet	Irland
Stadt	Galway
Zeitraum	30/08/23 → 1/09/23
Internetadresse	https://www.universityofgalway.ie/c3i/news-&-events/imvip_2023/

Research Field

Computer Vision

Zugriff auf Dokument

IMVIP_2023_ID26_DF2023_final_submissionEndgültige, publizierte Fassung, 1,23 MB

Andere Dateien und Links

https://iprcs.github.io/pdf/IMVIP2023_Proceeding.pdf

Diese Publikation zitieren

@inproceedings{b4b04bc65b2f479a9f34eda7b3282822,

title = "DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection",

abstract = "The deliberate manipulation of public opinion, especially through altered images, which are frequently disseminated through online social networks, poses a significant danger to society. To fight this issue on a technical level we support the research community by releasing the Digital Forensics 2023 (DF2023) training and validation dataset, comprising one million images from four major forgery categories: splicing, copy-move, enhancement and removal. This dataset enables objective comparison of network architectures and significantly reduces researchers{\textquoteright} time effort for preparing datasets.",

keywords = "Computer Vision",

author = "David Fischinger and Martin Boyer",

year = "2023",

language = "English",

isbn = "978-0-9934207-8-8",

pages = "128--135",

editor = "Peter Corcoran",

booktitle = "DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection",

note = "25th Irish Machine Vision and Image Processing Conference, IMVIP 2023 ; Conference date: 30-08-2023 Through 01-09-2023",

url = "https://www.universityofgalway.ie/c3i/news-&-events/imvip_2023/",

}

DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection. / Fischinger, David ; Boyer, Martin.
DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection. Hrsg. / Peter Corcoran. https://iprcs.github.io/pdf/IMVIP2023_Proceeding.pdf, 2023. S. 128-135.

Publikation: Beitrag in Buch oder Tagungsband › Vortrag mit Beitrag in Tagungsband › Begutachtung

TY - GEN

T1 - DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection

AU - Fischinger, David

AU - Boyer, Martin

PY - 2023

Y1 - 2023

N2 - The deliberate manipulation of public opinion, especially through altered images, which are frequently disseminated through online social networks, poses a significant danger to society. To fight this issue on a technical level we support the research community by releasing the Digital Forensics 2023 (DF2023) training and validation dataset, comprising one million images from four major forgery categories: splicing, copy-move, enhancement and removal. This dataset enables objective comparison of network architectures and significantly reduces researchers’ time effort for preparing datasets.

AB - The deliberate manipulation of public opinion, especially through altered images, which are frequently disseminated through online social networks, poses a significant danger to society. To fight this issue on a technical level we support the research community by releasing the Digital Forensics 2023 (DF2023) training and validation dataset, comprising one million images from four major forgery categories: splicing, copy-move, enhancement and removal. This dataset enables objective comparison of network architectures and significantly reduces researchers’ time effort for preparing datasets.

KW - Computer Vision

UR - https://iprcs.github.io/pdf/IMVIP2023_Proceeding.pdf

M3 - Conference Proceedings with Oral Presentation

SN - 978-0-9934207-8-8

SP - 128

EP - 135

BT - DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection

A2 - Corcoran, Peter

CY - https://iprcs.github.io/pdf/IMVIP2023_Proceeding.pdf

T2 - 25th Irish Machine Vision and Image Processing Conference

Y2 - 30 August 2023 through 1 September 2023

ER -

DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection

Abstract

Konferenz

Research Field

Zugriff auf Dokument

Andere Dateien und Links

Fingerprint

Diese Publikation zitieren