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Warwick image forensics dataset for device fingerprinting in multimedia forensics
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Quan, Yijun, Li, Chang-Tsun, Zhou, Yujue and Li, Li (2020) Warwick image forensics dataset for device fingerprinting in multimedia forensics. In: IEEE International conference on Multimedia and Expo, London, UK, 6-10 Jul 2020
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WRAP-Warwick-image-forensics-dataset-device-fingerprinting-multimedia-forensics-Li-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1392Kb) | Preview |
Abstract
Device fingerprints like sensor pattern noise (SPN) are widely used for provenance analysis and image authentication. Over the past few years, the rapid advancement in digital photography has greatly reshaped the pipeline of image capturing process on consumer-level mobile devices. The flexibility of camera parameter settings and the emergence of multiframe photography algorithms, especially high dynamic range (HDR) imaging, bring new challenges to device fingerprinting. The subsequent study on these topics requires a new purposefully built image dataset. In this paper, we present the Warwick Image Forensics Dataset, an image dataset of more than 58,600 images captured using 14 digital cameras with various exposure settings. Special attention to the exposure settings allows the images to be adopted by different multi-frame computational photography algorithms and for subsequent device fingerprinting. The dataset is released as an open-source, free for use for the digital forensic community.
| Item Type: | Conference Item (Paper) | ||||||
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| Subjects: | H Social Sciences > HV Social pathology. Social and public welfare Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TR Photography |
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| Divisions: | Faculty of Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Multimedia systems -- Security measures, Data encryption (Computer science) , Fingerprints, Fingerprints -- Identification -- Technological innovations, Photography -- Digital techniques | ||||||
| Publisher: | IEEE | ||||||
| Official Date: | 2020 | ||||||
| Dates: |
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| Date of first compliant deposit: | 19 May 2020 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Publisher Statement: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | ||||||
| Title of Event: | IEEE International conference on Multimedia and Expo | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | London, UK | ||||||
| Date(s) of Event: | 6-10 Jul 2020 | ||||||
| Related URLs: |
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