Image Classification on CIFAR (accuracy)
91.77AccuracyState 0 (uninfected)
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| State 0 (uninfected)Model=Inception, Learning Mode=std2024.09 | 91.77 | 1.75 | |
| State 0 (uninfected)Model=ResNet, Learning Mode=std2024.09 | 91.71 | 1.94 | |
| State 1 (infected)Model=ResNet, Learning Mode=std2024.09 | 91.26 | 99.96 | |
| State 1 (infected)Model=Inception, Learning Mode=std2024.09 | 90.91 | 99.93 | |
| State 0 (uninfected)Model=VGG, Learning Mode=std2024.09 | 90.85 | 0.96 | |
| State 1 (infected)Model=VGG, Learning Mode=std2024.09 | 90.28 | 99.93 | |
| Fine-TuningModel=VGG, Learning Mode=std2024.09 | 84.65 | 100 | |
| TaborModel=VGG, Learning Mode=std2024.09 | 84.58 | 85.07 | |
| Psycho-PassModel=VGG, Learning Mode=std2024.09 | 83.89 | 1.21 | |
| State 0 (uninfected)Model=Inception, Learning Mode=adv2024.09 | 83.6 | 0.21 | |
| Neural CleanseModel=VGG, Learning Mode=std2024.09 | 83.37 | 24.43 | |
| Pixel BackdoorModel=VGG, Learning Mode=std2024.09 | 83.32 | 5.49 | |
| State 1 (infected)Model=Inception, Learning Mode=adv2024.09 | 83.28 | 100 | |
| State 0 (uninfected)Model=ResNet, Learning Mode=adv2024.09 | 83.09 | 1.62 | |
| KDEPData=100%, Epoch=90, Time (/h)=40, Teacher Backbone=ResNet-50, Student Backbone=ResNet-18, Evaluation Protocol=fine-tuned2022.03 | 82.89 | — | |
| KDEPData=100%, Epoch=90, Time (/h)=43, Fine-tuning=true, Pre-trained Backbone=ResNet-50, Student Architecture=MobileNetV22022.03 | 82.73 | — | |
| KDEPData=10%, Epoch=900, Time (/h)=40, Teacher Backbone=ResNet-50, Student Backbone=ResNet-18, Evaluation Protocol=fine-tuned2022.03 | 82.64 | — | |
| KDEPData=10%, Epoch=900, Time (/h)=43, Fine-tuning=true, Pre-trained Backbone=ResNet-50, Student Architecture=MobileNetV22022.03 | 82.53 | — | |
| KDEPData=100%, Epoch=18, Time (/h)=8, Teacher Backbone=ResNet-50, Student Backbone=ResNet-18, Evaluation Protocol=fine-tuned2022.03 | 82.47 | — | |
| KDEPData=10%, Epoch=180, Time (/h)=8, Teacher Backbone=ResNet-50, Student Backbone=ResNet-18, Evaluation Protocol=fine-tuned2022.03 | 82.23 | — | |
| KDEPData=100%, Epoch=9, Time (/h)=4, Teacher Backbone=ResNet-50, Student Backbone=ResNet-18, Evaluation Protocol=fine-tuned2022.03 | 82.22 | — | |
| KDEPData=100%, Epoch=18, Time (/h)=8.6, Fine-tuning=true, Pre-trained Backbone=ResNet-50, Student Architecture=MobileNetV22022.03 | 82.15 | — | |
| SP. o.Data=100%, Epoch=90, Time (/h)=39, Teacher Backbone=ResNet-50, Student Backbone=ResNet-18, Evaluation Protocol=fine-tuned2022.03 | 82.08 | — | |
| KDEPData=10%, Epoch=180, Time (/h)=8.6, Fine-tuning=true, Pre-trained Backbone=ResNet-50, Student Architecture=MobileNetV22022.03 | 81.98 | — | |
| SP. o.Data=100%, Epoch=90, Time (/h)=42, Fine-tuning=true, Pre-trained Backbone=ResNet-50, Student Architecture=MobileNetV22022.03 | 81.91 | — | |
| State 1 (infected)Model=ResNet, Learning Mode=adv2024.09 | 81.63 | 100 | |
| KDEPData=10%, Epoch=90, Time (/h)=4, Teacher Backbone=ResNet-50, Student Backbone=ResNet-18, Evaluation Protocol=fine-tuned2022.03 | 81.61 | — | |
| KDEPData=100%, Epoch=9, Time (/h)=4.3, Fine-tuning=true, Pre-trained Backbone=ResNet-50, Student Architecture=MobileNetV22022.03 | 81.47 | — | |
| SP. b.Data=100%, Epoch=18, Time (/h)=7.8, Teacher Backbone=ResNet-50, Student Backbone=ResNet-18, Evaluation Protocol=fine-tuned2022.03 | 81.43 | — | |
| KDEPData=10%, Epoch=90, Time (/h)=4.3, Fine-tuning=true, Pre-trained Backbone=ResNet-50, Student Architecture=MobileNetV22022.03 | 81.06 | — | |
| SP. b.Data=100%, Epoch=9, Time (/h)=3.9, Teacher Backbone=ResNet-50, Student Backbone=ResNet-18, Evaluation Protocol=fine-tuned2022.03 | 80.27 | — | |
| SP. b.Data=100%, Epoch=18, Time (/h)=8.4, Fine-tuning=true, Pre-trained Backbone=ResNet-50, Student Architecture=MobileNetV22022.03 | 80.13 | — | |
| UnicornModel=VGG, Learning Mode=std2024.09 | 79.89 | 81.51 | |
| SP. b.Data=10%, Epoch=900, Time (/h)=42, Fine-tuning=true, Pre-trained Backbone=ResNet-50, Student Architecture=MobileNetV22022.03 | 79.83 | — | |
| TaborModel=ResNet, Learning Mode=std2024.09 | 79.81 | 80.69 | |
| SP. b.Data=10%, Epoch=180, Time (/h)=8.4, Fine-tuning=true, Pre-trained Backbone=ResNet-50, Student Architecture=MobileNetV22022.03 | 79.5 | — | |
| Fine-TuningModel=Inception, Learning Mode=std2024.09 | 79.5 | 100 | |
| SP. b.Data=10%, Epoch=900, Time (/h)=39, Teacher Backbone=ResNet-50, Student Backbone=ResNet-18, Evaluation Protocol=fine-tuned2022.03 | 79.49 | — | |
| TaborModel=Inception, Learning Mode=std2024.09 | 79.38 | 99.62 | |
| SP. b.Data=10%, Epoch=180, Time (/h)=7.8, Teacher Backbone=ResNet-50, Student Backbone=ResNet-18, Evaluation Protocol=fine-tuned2022.03 | 79.34 | — | |
| State 1 (infected)Model=VGG, Learning Mode=adv2024.09 | 79.3 | 100 | |
| SP. b.Data=10%, Epoch=90, Time (/h)=3.9, Teacher Backbone=ResNet-50, Student Backbone=ResNet-18, Evaluation Protocol=fine-tuned2022.03 | 79.29 | — | |
| SP. b.Data=100%, Epoch=9, Time (/h)=4.2, Fine-tuning=true, Pre-trained Backbone=ResNet-50, Student Architecture=MobileNetV22022.03 | 78.95 | — | |
| Psycho-PassModel=ResNet, Learning Mode=std2024.09 | 78.94 | 20.37 | |
| State 0 (uninfected)Model=VGG, Learning Mode=adv2024.09 | 78.84 | 1.04 | |
| SP. b.Data=10%, Epoch=90, Time (/h)=4.2, Fine-tuning=true, Pre-trained Backbone=ResNet-50, Student Architecture=MobileNetV22022.03 | 78.6 | — | |
| Fine-TuningModel=ResNet, Learning Mode=std2024.09 | 78.56 | 73.68 | |
| Pixel BackdoorModel=Inception, Learning Mode=std2024.09 | 77.61 | 88.31 | |
| rand. init.Teacher Backbone=ResNet-50, Student Backbone=ResNet-18, Evaluation Protocol=fine-tuned2022.03 | 77.34 | — | |
| Pixel BackdoorModel=ResNet, Learning Mode=std2024.09 | 77.28 | 33.59 | |
| rand. init.Fine-tuning=true, Pre-trained Backbone=None, Student Architecture=MobileNetV22022.03 | 76.66 | — | |
| UnicornModel=Inception, Learning Mode=std2024.09 | 76.46 | 50.66 | |
| Psycho-PassModel=Inception, Learning Mode=std2024.09 | 76.15 | 7.09 | |
| Neural CleanseModel=Inception, Learning Mode=std2024.09 | 76.04 | 95.99 | |
| Fine-TuningModel=Inception, Learning Mode=adv2024.09 | 75.13 | 100 | |
| Neural CleanseModel=ResNet, Learning Mode=std2024.09 | 74.27 | 74.48 | |
| Psycho-PassModel=Inception, Learning Mode=adv2024.09 | 73.95 | 5.14 | |
| UnicornModel=ResNet, Learning Mode=std2024.09 | 73.43 | 50.4 | |
| TaborModel=Inception, Learning Mode=adv2024.09 | 73.16 | 100 | |
| Neural CleanseModel=Inception, Learning Mode=adv2024.09 | 72.31 | 100 | |
| UnicornModel=Inception, Learning Mode=adv2024.09 | 69.05 | 100 | |
| Pixel BackdoorModel=Inception, Learning Mode=adv2024.09 | 68.91 | 9.1 | |
| CTL-MST (Optimization)T=200, B=20002026.01 | 68.6 | — | |
| CTL-MST (Feature)T=200, B=20002026.01 | 67.9 | — | |
| IndivT=200, B=20002026.01 | 67.7 | — | |
| CTL-RandTreeT=200, B=20002026.01 | 67.3 | — | |
| CTL-MST (Target)T=200, B=20002026.01 | 67.1 | — | |
| StarT=200, B=20002026.01 | 66.7 | — | |
| CTL-MST (Optimization)T=200, B=5002026.01 | 66.6 | — | |
| CTL-MST (Feature)T=200, B=5002026.01 | 66 | — | |
| CTL-RandTreeT=200, B=5002026.01 | 65.2 | — | |
| CTL-MST (Target)T=200, B=5002026.01 | 64.8 | — | |
| Fine-TuningModel=VGG, Learning Mode=adv2024.09 | 64.72 | 99.91 | |
| Fine-TuningModel=ResNet, Learning Mode=adv2024.09 | 64.22 | 97.82 | |
| StarT=200, B=5002026.01 | 64 | — | |
| Psycho-PassModel=VGG, Learning Mode=adv2024.09 | 63.04 | 3.53 | |
| Psycho-PassModel=ResNet, Learning Mode=adv2024.09 | 63.02 | 9.24 | |
| TaborModel=ResNet, Learning Mode=adv2024.09 | 62.04 | 95.95 | |
| TaborModel=VGG, Learning Mode=adv2024.09 | 61.63 | 91.45 | |
| Neural CleanseModel=ResNet, Learning Mode=adv2024.09 | 61.58 | 59.37 | |
| IndivT=200, B=5002026.01 | 60.9 | — | |
| Neural CleanseModel=VGG, Learning Mode=adv2024.09 | 60.44 | 83.28 | |
| Pixel BackdoorModel=VGG, Learning Mode=adv2024.09 | 58.99 | 9.19 | |
| Pixel BackdoorModel=ResNet, Learning Mode=adv2024.09 | 58.09 | 59.9 | |
| UnicornModel=ResNet, Learning Mode=adv2024.09 | 57.97 | 99.33 | |
| UnicornModel=VGG, Learning Mode=adv2024.09 | 57.15 | 10.02 |