Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A CNN based architecture for forgery detection in administrative documents
University of Biskra, Algeria.
University of Biskra, Algeria.
University of Biskra, Algeria.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-4390-411X
2022 (English)In: 2022 International Symposium on iNnovative Informatics of Biskra, ISNIB 2022, Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 135-140Conference paper, Published paper (Refereed)
Abstract [en]

The use of digital documents is knowing a widespread in different daily administrative and economic transactions. Simultaneously, the forgery of many documents becomes a crime that costs billions to states and companies. Several researchers tried to develop techniques that automatically detect forged documents using machine learning and image processing. With the immense success of deep learning applications, we employ, in this work, a convolutional neural network architecture that uses a gathered dataset of forged and authentic administrative documents. The results obtained on our dataset of 493 documents reached 73.95% accuracy and 97.3% recall, surpassing the efficiency of the machine learning base methods. © 2022 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022. p. 135-140
Keywords [en]
Deep learning, Forgery detection, Image processing, Convolutional neural networks, Learning systems, Network architecture, CNN-based architecture, Convolutional neural network, Digital Documents, Economic transactions, Forgery detections, Images processing, Machine-learning, Neural network architecture
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-24483DOI: 10.1109/ISNIB57382.2022.10076089ISI: 000986982400024Scopus ID: 2-s2.0-85152434951ISBN: 9798350320657 (print)OAI: oai:DiVA.org:bth-24483DiVA, id: diva2:1753789
Conference
2022 International Symposium on iNnovative Informatics of Biskra, ISNIB 2022, Biskra, 7 December 2022 through 8 December 2022
Available from: 2023-04-28 Created: 2023-04-28 Last updated: 2023-06-12Bibliographically approved

Open Access in DiVA

fulltext(563 kB)1370 downloads
File information
File name FULLTEXT01.pdfFile size 563 kBChecksum SHA-512
98f4cfc880a9351278cd56b5e4854e34c1cb77041b8b48f62e94e12caa708f650ebc7c84bb6fbd59011925456268b6eeb45b1da74ef9615d00bfac8e90285f4b
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Cheddad, Abbas

Search in DiVA

By author/editor
Cheddad, Abbas
By organisation
Department of Computer Science
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 1372 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 193 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf