Towards a Systematic Screening Tool for Quality Assurance and Semiautomatic Fraud Detection for Images in the Life Sciences.
The quality and authenticity of images is essential for data presentation, especially in the life sciences. Questionable images may often be a first indicator for questionable results, too. Therefore, a tool that uses mathematical methods to detect suspicious images in large image archives can be a...
| Publicado en: | Science & Engineering Ethics Vol. 23; no. 4; pp. 1113 - 1129 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Aug2017
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=124430990&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 124430990 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 13533452 GNI jtl: Science & Engineering Ethics issn: 13533452 maglogo: N pubinfo: dt: Aug2017 vid: 23 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 124430990 10.1007/s11948-016-9841-7 ppf: 1113 ppct: 16 formats: fmt: @attributes: type: P size: 3.2MB tig: atl: Towards a Systematic Screening Tool for Quality Assurance and Semiautomatic Fraud Detection for Images in the Life Sciences. aug: au: Koppers, Lars Wormer, Holger Ickstadt, Katja affil: Department of Statistics , TU Dortmund University , Vogelpothsweg 87 44227 Dortmund Germany Institute for Journalism , TU Dortmund University , Emil-Figge-Straße 50 44227 Dortmund Germany su: Digital images Image processing Quality assurance Life sciences Fraud sug: subj: Digital images Image processing Quality assurance Life sciences Fraud keyword: Digital image Ethics Fraud detection Manipulation ab: The quality and authenticity of images is essential for data presentation, especially in the life sciences. Questionable images may often be a first indicator for questionable results, too. Therefore, a tool that uses mathematical methods to detect suspicious images in large image archives can be a helpful instrument to improve quality assurance in publications. As a first step towards a systematic screening tool, especially for journal editors and other staff members who are responsible for quality assurance, such as laboratory supervisors, we propose a basic classification of image manipulation. Based on this classification, we developed and explored some simple algorithms to detect copied areas in images. Using an artificial image and two examples of previously published modified images, we apply quantitative methods such as pixel-wise comparison, a nearest neighbor and a variance algorithm to detect copied-and-pasted areas or duplicated images. We show that our algorithms are able to detect some simple types of image alteration, such as copying and pasting background areas. The variance algorithm detects not only identical, but also very similar areas that differ only by brightness. Further types could, in principle, be implemented in a standardized scanning routine. We detected the copied areas in a proven case of image manipulation in Germany and showed the similarity of two images in a retracted paper from the Kato labs, which has been widely discussed on sites such as pubpeer and retraction watch. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Science & Engineering Ethics is a copyright of Springer, 2017. All Rights Reserved. item: Science & Engineering Ethics holder: Springer Nature dt: @attributes: year: 2017 holdings: @attributes: islocal: N |
|---|