Benchmarking Scientific Image Forgery Detectors.
The field of scientific image integrity presents a challenging research bottleneck given the lack of available datasets to design and evaluate forensic techniques. The sensitivity of data also creates a legal hurdle that restricts the use of real-world cases to build any accessible forensic benchmar...
| Publicado en: | Science & Engineering Ethics Vol. 28; no. 4; pp. 1 - 31 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Aug2022
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| 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=158434752&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 158434752 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 13533452 GNI jtl: Science & Engineering Ethics issn: 13533452 maglogo: N pubinfo: dt: Aug2022 vid: 28 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 158434752 10.1007/s11948-022-00391-4 ppf: 1 ppct: 30 formats: fmt: – @attributes: type: T – @attributes: type: P size: 5.9MB tig: atl: Benchmarking Scientific Image Forgery Detectors. aug: au: Cardenuto, João P. Rocha, Anderson affil: Artificial Intelligence Lab. (Recod.ai), Institute of Computing, University of Campinas, Av. Albert Einstein, 1251 - Cidade Universitária, 13083-852, Campinas, SP, Brazil sug: keyword: Computational scientific integrity Image manipulation Misconduct detection Scientific integrity benchmark ab: The field of scientific image integrity presents a challenging research bottleneck given the lack of available datasets to design and evaluate forensic techniques. The sensitivity of data also creates a legal hurdle that restricts the use of real-world cases to build any accessible forensic benchmark. In light of this, there is no comprehensive understanding on the limitations and capabilities of automatic image analysis tools for scientific images, which might create a false sense of data integrity. To mitigate this issue, we present an extendable open-source algorithm library that reproduces the most common image forgery operations reported by the research integrity community: duplication, retouching, and cleaning. We create a large scientific forgery image benchmark (39,423 images) with enriched ground truth using this library and realistic scientific images. All figures within the benchmark are synthetically doctored using images collected from creative commons sources. While collecting the source images, we ensured that the they did not present any suspicious integrity problems. Because of the high number of retracted papers due to image duplication, this work evaluates the state-of-the-art copy-move detection methods in the proposed dataset, using a new metric that asserts consistent match detection between the source and the copied region. All evaluated methods had a low performance in this dataset, indicating that scientific images might need a specialized copy-move detector. The dataset and source code are available at . pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Science & Engineering Ethics is a copyright of Springer, 2022. All Rights Reserved. item: Science & Engineering Ethics holder: Springer Nature dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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