Identity Documents Authentication based on Forgery Detection of Guilloche Pattern
Musab Al-Ghadi, Zuheng Ming, Petra Gomez-Kr\"amer, Jean-Christophe, Burie

TL;DR
This paper proposes a CNN-based method for authenticating identity documents by detecting forgery in guilloche patterns, combining feature extraction and similarity measurement to improve forgery detection accuracy.
Contribution
The paper introduces a novel CNN-based approach that jointly extracts discriminative features and measures similarity for forgery detection in guilloche patterns of identity documents.
Findings
High accuracy in distinguishing authentic and forged documents
Effective feature extraction capturing guilloche pattern characteristics
Demonstrated robustness on MIDV-2020 dataset
Abstract
In cases such as digital enrolment via mobile and online services, identity document verification is critical in order to efficiently detect forgery and therefore build user trust in the digital world. In this paper, an authentication model for identity documents based on forgery detection of guilloche patterns is proposed. The proposed approach is made up of two steps: feature extraction and similarity measure between a pair of feature vectors of identity documents. The feature extraction step involves learning the similarity between a pair of identity documents via a convolutional neural network (CNN) architecture and ends by extracting highly discriminative features between them. While, the similarity measure step is applied to decide if a given identity document is authentic or forged. In this work, these two steps are combined together to achieve two objectives: (i) extracted…
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Taxonomy
TopicsDigital Media Forensic Detection · Handwritten Text Recognition Techniques · Digital and Cyber Forensics
