LBRT: Local-Information-Refined Transformer for Image Copy-Move Forgery Detection.
Sensors (Basel)
; 24(13)2024 Jun 26.
Article
em En
| MEDLINE
| ID: mdl-39000921
ABSTRACT
The current deep learning methods for copy-move forgery detection (CMFD) are mostly based on deep convolutional neural networks, which frequently discard a large amount of detail information throughout convolutional feature extraction and have poor long-range information extraction capabilities. The Transformer structure is adept at modeling global context information, but the patch-wise self-attention calculation still neglects the extraction of details in local regions that have been tampered with. A local-information-refined dual-branch network, LBRT (Local Branch Refinement Transformer), is designed in this study. It performs Transformer encoding on the global patches segmented from the image and local patches re-segmented from the global patches using a global modeling branch and a local refinement branch, respectively. The self-attention features from both branches are precisely fused, and the fused feature map is then up-sampled and decoded. Therefore, LBRT considers both global semantic information modeling and local detail information refinement. The experimental results show that LBRT outperforms several state-of-the-art CMFD methods on the USCISI dataset, CASIA CMFD dataset, and DEFACTO CMFD dataset.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2024
Tipo de documento:
Article