| Dataset Name | SOTA Method | Metric | Trend | ||
|---|---|---|---|---|---|
| ImageNet | RDI-FTM-E | VGG-16 Score88.2 | 39 | 4d ago | |
| ImageNet | MI-TI-DI+RAP-LS | Dense-121 Score88.5 | 31 | 4d ago | |
| ImageNet 1000 images (val) | SA | Clean Accuracy77.6 | 24 | 4d ago | |
| ImageNet (val) | RDI-FTM-E | ViT Performance1,840 | 23 | 4d ago | |
| CIFAR-10 | PI-Attack | ASR94.22 | 20 | 4d ago | |
| MNIST | DST | ASR73.85 | 19 | 4d ago | |
| CIFAR-100 | DST | ASR23.01 | 16 | 4d ago | |
| ImageNet 10-Targets (all-source) | MTDSI+RAP-LS | Targeted Attack Success Rate95.7 | 15 | 4d ago | |
| ImageNet-Compatible | DI | Success Rate (adv-RN-50)98.9 | 14 | 4d ago | |
| ModelNet40 (test) | e-ISO | ASR0.9958 | 12 | 4d ago | |
| CIFAR-10 (test) | ECA | Control Error (ξ)4 | 12 | 4d ago | |
| NTU | BASAR | Attack Success Rate100 | 12 | 4d ago | |
| HDM05 | BASAR | Attack Success Rate100 | 12 | 4d ago | |
| ImageNet RN-50 Source 1k (val) | RDI-FTM-E | ViT Performance Score6.8 | 10 | 4d ago | |
| ImageNet | RDI-FTM-E | VGG-16 Robust Accuracy34.2 | 10 | 4d ago | |
| ImageNet | RDI-FTM-E | VGG-16 Score90 | 9 | 4d ago | |
| ImageNet (test) | RDI | Inference Time (s)1.76 | 9 | 4d ago | |
| ImageNet-1K (val) | ResNet-18 Result9.18 | 9 | 4d ago | ||
| ILSVRC 2012 | Median Noise Magnitude152.296 | 7 | 4d ago | ||
| CIFAR-100 (test) | C&W l2 | ASR (%)99.99 | 7 | 4d ago | |
| CIFAR-10 | CFM-RDI | VGG-16 Robustness Score98.3 | 6 | 4d ago | |
| TinyImageNet | BASES | Fooling Rate99.7 | 6 | 4d ago | |
| ImageNet 1k (test) | MTDAI+RAP-LS | Model Performance (IncRes-v2)90.4 | 6 | 4d ago | |
| ImageNet-compatible 100 images | RDI-FTM-E | Success Count47 | 5 | 4d ago | |
| Diabetic Retinopathy | PI-Attack | Average Queries98.5 | 4 | 4d ago |
