Image Classification on ImageNet-1k (Adversarial Robustness)
56.29Clean AccuracyRS-FGSM
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| RS-FGSMBackbone=ResNet-50, Epsilon=2/255, Attack Protocol=PGD-50-10, Training Epochs=15, Pre-training=ImageNet-1k2025.05 | 56.29 | 36.86 | |
| FGSMBackbone=ResNet-50, Epsilon=2/255, Attack Protocol=PGD-50-10, Training Epochs=15, Pre-training=ImageNet-1k2025.05 | 54.72 | 38.21 | |
| N-FGSMBackbone=ResNet-50, Epsilon=2/255, Attack Protocol=PGD-50-10, Training Epochs=15, Pre-training=ImageNet-1k2025.05 | 54.39 | 38.07 | |
| lp-FGSMBackbone=ResNet-50, Epsilon=2/255, Attack Protocol=PGD-50-10, Training Epochs=15, Pre-training=ImageNet-1k2025.05 | 53.18 | 37.94 | |
| RS-FGSMBackbone=ResNet-50, Epsilon=4/255, Attack Protocol=PGD-50-10, Training Epochs=15, Pre-training=ImageNet-1k2025.05 | 50.81 | 25.12 | |
| lp-FGSMBackbone=ResNet-50, Epsilon=6/255, Attack Protocol=PGD-50-10, Training Epochs=15, Pre-training=ImageNet-1k2025.05 | 48.61 | 19.79 | |
| FGSMBackbone=ResNet-50, Epsilon=6/255, Attack Protocol=PGD-50-10, Training Epochs=15, Pre-training=ImageNet-1k2025.05 | 48.55 | 0.08 | |
| FGSMBackbone=ResNet-50, Epsilon=4/255, Attack Protocol=PGD-50-10, Training Epochs=15, Pre-training=ImageNet-1k2025.05 | 48.5 | 25.86 | |
| lp-FGSMBackbone=ResNet-50, Epsilon=4/255, Attack Protocol=PGD-50-10, Training Epochs=15, Pre-training=ImageNet-1k2025.05 | 48.42 | 28.35 | |
| N-FGSMBackbone=ResNet-50, Epsilon=6/255, Attack Protocol=PGD-50-10, Training Epochs=15, Pre-training=ImageNet-1k2025.05 | 47.7 | 17.12 | |
| RS-FGSMBackbone=ResNet-50, Epsilon=6/255, Attack Protocol=PGD-50-10, Training Epochs=15, Pre-training=ImageNet-1k2025.05 | 47.67 | 16.49 | |
| N-FGSMBackbone=ResNet-50, Epsilon=4/255, Attack Protocol=PGD-50-10, Training Epochs=15, Pre-training=ImageNet-1k2025.05 | 47.56 | 26.28 |