Research Article
Adversarial Sample Detection with Gaussian Mixture Conditional Generative Adversarial Networks
Table 3
Unsupervised scenarios for detecting adversarial samples.
| Model | Dataset | Method | FGSM | Unsupervised scenario | BIM | DeepFool | CW | PGD |
| DenseNet | CIFAR10 | Odds-testing | 45.23 | 69.01 | 58.30 | 61.29 | 97.93 | GM-CGAN | 87.89 | 73.69 | 80.81 | 78.12 | 46.45 | CIFAR100 | Odds-testing | 43.22 | 65.22 | 49.53 | 47.64 | 96.91 | GM-CGAN | 98.09 | 68.34 | 74.42 | 65.72 | 41.09 | SVHN | Odds-testing | 56.14 | 71.11 | 67.81 | 70.71 | 99.25 | GM-CGAN | 83.35 | 73.56 | 81.86 | 80.16 | 46.40 |
| ResNet | CIFAR10 | Odds-testing | 46.32 | 59.85 | 75.58 | 57.58 | 96.18 | GM-CGAN | 96.68 | 64.74 | 79.48 | 73.49 | 98.80 | CIFAR100 | Odds-testing | 38.26 | 43.52 | 61.13 | 44.74 | 93.73 | GM-CGAN | 80.96 | 81.20 | 80.35 | 67.56 | 91.30 | SVHN | Odds-testing | 65.09 | 70.31 | 77.05 | 72.12 | 99.08 | GM-CGAN | 94.97 | 89.04 | 94.71 | 83.41 | 97.52 |
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