Model Inversion on CelebA (test)
93.2Attack AccuracyKEDMI + LOMMA
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| KEDMI + LOMMATarget Model=face.evoLve, Modification=Logit Maximization + Model Augmentation2023.04 | 93.2 | 11.8 | 1,154.32 | |
| KEDMI + LOMMATarget Model=IR152, Modification=Logit Maximization + Model Augmentation2023.04 | 92.93 | 12.4 | 1,138.62 | |
| KEDMI + LOMTarget Model=face.evoLve, Modification=Logit Maximization2023.04 | 92.53 | 11.13 | 1,183.76 | |
| KEDMI + LOMTarget Model=IR152, Modification=Logit Maximization2023.04 | 92.47 | 11.94 | 1,168.55 | |
| KEDMI + LOMMATarget Model=VGG16, Modification=Logit Maximization + Model Augmentation2023.04 | 90.27 | 16.27 | 1,147.41 | |
| KEDMI + LOMTarget Model=VGG16, Modification=Logit Maximization2023.04 | 89.07 | 15.07 | 1,218.46 | |
| KEDMI + MATarget Model=face.evoLve, Modification=Model Augmentation2023.04 | 85.07 | 3.67 | 1,222.02 | |
| KEDMI + MATarget Model=IR152, Modification=Model Augmentation2023.04 | 84.73 | 4.2 | 1,220.23 | |
| KED-MIModel Architecture=IR152, Threat Model=Whitebox2022.03 | 83 | — | — | |
| GMI + LOMMATarget Model=IR152, Modification=Logit Maximization + Model Augmentation2023.04 | 82.4 | 51.8 | 1,254.32 | |
| GMI + LOMMATarget Model=face.evoLve, Modification=Logit Maximization + Model Augmentation2023.04 | 82.33 | 55.26 | 1,257.5 | |
| KED-MIModel Architecture=FaceNet64, Threat Model=Whitebox2022.03 | 82 | — | — | |
| KEDMI + MATarget Model=VGG16, Modification=Model Augmentation2023.04 | 82 | 8 | 1,248.33 | |
| KEDMITarget Model=face.evoLve2023.04 | 81.4 | — | 1,248.32 | |
| KEDMITarget Model=IR1522023.04 | 80.53 | — | 1,247.28 | |
| GMI + LOMTarget Model=IR152, Modification=Logit Maximization2023.04 | 78.53 | 47.93 | 1,289.62 | |
| GMI + LOMMATarget Model=VGG16, Modification=Logit Maximization + Model Augmentation2023.04 | 77.6 | 58.53 | 1,296.26 | |
| BREP-MIModel Architecture=FaceNet64, Threat Model=Label-only2022.03 | 75.67 | — | — | |
| GMI + MATarget Model=face.evoLve, Modification=Model Augmentation2023.04 | 74.13 | 47.06 | 1,352.25 | |
| KEDMITarget Model=VGG162023.04 | 74 | — | 1,289.88 | |
| BREP-MIModel Architecture=IR152, Threat Model=Label-only2022.03 | 72 | — | — | |
| GMI + LOMTarget Model=VGG16, Modification=Logit Maximization2023.04 | 69.67 | 50.6 | 1,363.81 | |
| KED-MIModel Architecture=VGG16, Threat Model=Whitebox2022.03 | 69 | — | — | |
| BREP-MIModel Architecture=VGG16, Threat Model=Label-only2022.03 | 63.33 | — | — | |
| GMI + LOMTarget Model=face.evoLve, Modification=Logit Maximization2023.04 | 61.67 | 34.6 | 1,405.35 | |
| GMI + MATarget Model=IR152, Modification=Model Augmentation2023.04 | 61.2 | 30.6 | 1,389.99 | |
| GMI + MATarget Model=VGG16, Modification=Model Augmentation2023.04 | 51.73 | 32.66 | 1,467.68 | |
| GMIModel Architecture=FaceNet64, Threat Model=Whitebox2022.03 | 32 | — | — | |
| GMITarget Model=IR1522023.04 | 30.6 | — | 1,609.29 | |
| GMITarget Model=face.evoLve2023.04 | 27.07 | — | 1,635.87 | |
| GMIModel Architecture=IR152, Threat Model=Whitebox2022.03 | 26 | — | — | |
| GMITarget Model=VGG162023.04 | 19.07 | — | 1,715.6 | |
| GMIModel Architecture=VGG16, Threat Model=Whitebox2022.03 | 15 | — | — | |
| LB-MIModel Architecture=FaceNet64, Threat Model=Blackbox2022.03 | 1.67 | — | — | |
| LB-MIModel Architecture=VGG16, Threat Model=Blackbox2022.03 | 1.33 | — | — | |
| LB-MIModel Architecture=IR152, Threat Model=Blackbox2022.03 | 0.33 | — | — |