From: On the role of deep learning model complexity in adversarial robustness for medical images
Attack | CBR-LargeT | Resnet-8 | Resnet-20 | Resnet-32 | Resnet-50 |
---|---|---|---|---|---|
(a) Chest X-Ray Accuracy(%), \(\epsilon\) = 1 | |||||
No Attack | 96.43 + − 1.84 | 97.43 + − 1.01 | 97.46 + − 1.24 | 97.41 + − 0.98 | 96.90 + − 1.26 |
FGSM | 88.83 + − 2.14 | 24.63 + − 11.12 | 15.13 + − 10.68 | 5.77 + − 3.30 | 9.37 + − 5.82 |
PGD | 88.37 + − 2.31 | 15.07 + − 9.95 | 7.10 + − 9.26 | 0.43 + − 0.62 | 0.83 + − 1.23 |
(b) Dermoscopy Accuracy (%), \(\epsilon\) = 0.1 | |||||
No Attack | 96.40 + − 0.95 | 95.53 + − 1.02 | 95.03 + − 1.16 | 95.07 + − 1.48 | 95.20 + − 0.85 |
FGSM | 92.80 + − 1.48 | 66.30 + − 12.37 | 49.37 + − 5.70 | 53.23 + − 13.13 | 49.10 + − 7.28 |
PGD | 92.63 + − 1.66 | 53.30 + − 10.61 | 38.17 + − 7.74 | 41.83 + − 15.77 | 43.63 + − 14.91 |
(c) OCT Accuracy (%), \(\epsilon\) = 2 | |||||
No Attack | 96.30 + − 0.67 | 95.53 + − 1.08 | 95.03 + − 0.81 | 95.07 + − 1.05 | 95.20 + − 2.71 |
FGSM | 88.58 + − 2.12 | 63.85 + − 6.56 | 63.00 + − 3.92 | 67.88 + − 3.94 | 68.65 + − 5.36 |
PGD | 78.35 + − 10.70 | 55.85 + − 8.60 | 35.23 + − 9.30 | 37.98 + − 8.23 | 36.13 + − 11.56 |