Face recognition on VGG-Face (test)
52.37AccuracyClean Model
Evaluation results
| Method | Links | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Clean Model2025.12 | 52.37 | — | — | — | — | — | — | 96 | |
| Abstract2025.12 | 51.93 | — | — | — | — | — | — | 10,000 | |
| ComMark2025.12 | 51.79 | — | — | — | — | — | — | 9,971 | |
| MAB2025.12 | 51.75 | — | — | — | — | — | — | 10,000 | |
| MEAD2025.12 | 51.66 | — | — | — | — | — | — | 9,800 | |
| BlindMark2025.12 | 51.59 | — | — | — | — | — | — | 9,980 | |
| Noise2025.12 | 50.38 | — | — | — | — | — | — | 9,991 | |
| Content2025.12 | 49.99 | — | — | — | — | — | — | 9,999 | |
| ContentAttack Type=Distillation, Label Setting=Soft Label, Backbone=ResNet-342025.12 | 8.09 | — | — | — | — | — | — | 99.84 | |
| ComMarkAttack Type=JBDA, Label Setting=Soft Label, Backbone=ResNet-342025.12 | 6.5 | — | — | — | — | — | — | 98.94 |