A CNN based architecture for forgery detection in administrative documents
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-24483 DOI: 10.1109/ISNIB57382.2022.10076089 ISI: 000986982400024 Scopus ID: 2-s2.0-85152434951 ISBN: 9798350320657 (print) OAI: oai:DiVA.org:bth-24483 DiVA, id: diva2:1753789
Conference 2022 International Symposium on iNnovative Informatics of Biskra, ISNIB 2022, Biskra, 7 December 2022 through 8 December 2022
2023-04-282023-04-282023-06-12 Bibliographically approved