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MC-LCR: Multi-modal contrastive classification by locally correlated representations for effective face forgery detection
[article]
2022
arXiv
pre-print
As the remarkable development of facial manipulation technologies is accompanied by severe security concerns, face forgery detection has become a recent research hotspot. Most existing detection methods train a binary classifier under global supervision to judge real or fake. However, advanced manipulations only perform small-scale tampering, posing challenges to comprehensively capture subtle and local forgery artifacts, especially in high compression settings and cross-dataset scenarios. To
arXiv:2110.03290v2
fatcat:kcpx3ndznve67f5rvzxz6djfj4