Towards Generalizable Deepfake Detection: An Inconsistency-Aware Vision-Language Model Tuning Approach.
- DOI
- 10.3390/s26144512
- Published
- 2026 Jul 16
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s26144512,
title = {Towards Generalizable Deepfake Detection: An Inconsistency-Aware Vision-Language Model Tuning Approach.},
author = {Zhang L and Peng S and Xu M and Lu T},
year = {2026},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s26144512},
url = {https://doi.org/10.3390/s26144512}
}RIS
TY - JOUR TI - Towards Generalizable Deepfake Detection: An Inconsistency-Aware Vision-Language Model Tuning Approach. AU - Zhang L AU - Peng S AU - Xu M AU - Lu T PY - 2026 JO - Sensors (Basel, Switzerland) DO - 10.3390/s26144512 UR - https://doi.org/10.3390/s26144512 ER -
APA
L, Z., S, P., M, X., & T, L. (2026). Towards Generalizable Deepfake Detection: An Inconsistency-Aware Vision-Language Model Tuning Approach.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26144512
Source records
- pubmed · retrieved 2026-09-26T15:08:23.341Z