Image Classification on SVHN (Natural and PGD-50-10 Robustness)
95.09Accuracy (Natural)RS-FGSM
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| RS-FGSMnoise magnitude=4/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 95.09 | 71.28 | |
| RS-AAERnoise magnitude=4/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 94.99 | 71.97 | |
| MultiGradnoise magnitude=8/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 94.86 | 11.49 | |
| ZeroGradnoise magnitude=4/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 94.81 | 71.59 | |
| MultiGradnoise magnitude=4/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 94.71 | 71.98 | |
| PGD-2noise magnitude=4/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 94.66 | 73.29 | |
| PGD-2noise magnitude=8/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 94.63 | 20.68 | |
| Grad Alignnoise magnitude=4/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 94.56 | 72.12 | |
| N-FGSMnoise magnitude=4/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 94.54 | 72.53 | |
| MultiGradnoise magnitude=12/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 94.48 | 0 | |
| RS-FGSMnoise magnitude=8/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 94.46 | 0 | |
| PGD-10noise magnitude=4/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 94.37 | 74.76 | |
| N-AAERnoise magnitude=4/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 94.35 | 73.1 | |
| PGD-2noise magnitude=12/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 94.16 | 0.02 | |
| FreeATnoise magnitude=4/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 93.66 | 71.61 | |
| RS-FGSMnoise magnitude=12/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 92.74 | 0 | |
| ZeroGradnoise magnitude=8/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 92.42 | 35.93 | |
| FreeATnoise magnitude=12/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 92.36 | 0 | |
| FreeATnoise magnitude=8/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 91.29 | 0.01 | |
| RS-AAERnoise magnitude=8/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 90.11 | 41.75 | |
| Grad Alignnoise magnitude=8/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 90.1 | 43.85 | |
| PGD-10noise magnitude=8/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 89.67 | 53.95 | |
| N-FGSMnoise magnitude=8/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 89.56 | 45.63 | |
| N-AAERnoise magnitude=8/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 89.26 | 46.98 | |
| ZeroGradnoise magnitude=12/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 88.09 | 14.14 | |
| Grad Alignnoise magnitude=12/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 84.01 | 23.62 | |
| RS-AAERnoise magnitude=12/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 83.5 | 22.84 | |
| N-AAERnoise magnitude=12/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 82.76 | 26.87 | |
| N-FGSMnoise magnitude=12/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 81.48 | 26.13 | |
| PGD-10noise magnitude=12/255, Backbone=PreActResNet-18, Threat model=L_inf, Number of random seeds=32024.04 | 80.08 | 37.65 |