SFTA-Net: a self-supervised approach to detect copy-move and splicing forgery to leverage triplet loss, auxiliary loss, and spatial attention.

Alabrah A

DOI: 10.7717/peerj-cs.2803
Published: 2025
Container: PeerJ. Computer science
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.7717/peerj-cs.2803,
  title = {SFTA-Net: a self-supervised approach to detect copy-move and splicing forgery to leverage triplet loss, auxiliary loss, and spatial attention.},
  author = {Alabrah A},
  year = {2025},
  journal = {PeerJ. Computer science},
  doi = {10.7717/peerj-cs.2803},
  url = {https://doi.org/10.7717/peerj-cs.2803}
}

RIS

TY  - JOUR
TI  - SFTA-Net: a self-supervised approach to detect copy-move and splicing forgery to leverage triplet loss, auxiliary loss, and spatial attention.
AU  - Alabrah A
PY  - 2025
JO  - PeerJ. Computer science
DO  - 10.7717/peerj-cs.2803
UR  - https://doi.org/10.7717/peerj-cs.2803
ER  -

APA

A, A. (2025). SFTA-Net: a self-supervised approach to detect copy-move and splicing forgery to leverage triplet loss, auxiliary loss, and spatial attention.. PeerJ. Computer science. https://doi.org/10.7717/peerj-cs.2803

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pubmed · retrieved 2026-09-25T01:15:10.271Z