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Image Classification on Tiny-ImageNet (Adversarial Robustness Suite)

60.26Clean AccuracyOriginal Attack
-1.474414.552830.5846.6072Jun 16, 2026Jun 17, 2026Jun 18, 2026

Evaluation results

MethodLinks
Original AttackQuantization level=INT82026.0660.26————————99.69
TRADESModel=ResNet-342026.0651110.411.828.14.712.910.711.4—
TaFDModel=ResNet-342026.0649.36.319.85.826.93.217.16.212.2—
Adaptive AttackQuantization level=INT82026.0647.81————————96.31
TaFDModel=MobileViT-XS2026.0647.47.521.516.429.26.52315.317.1—
FACEModel=ResNet-342026.0647.3000.40.3180.40.42.8—
TRADESModel=MobileViT-XS2026.0645.8115.47.523.92.714.76.710.3—
FACEModel=MobileViT-XS2026.0645.500.20.518.10.30.50.42.9—
Mean-ATModel=ResNet-342026.0645.13.62012.125.24.618.610.113.5—
MNGModel=ResNet-342026.0643.54.419.913.725.35.319.21114.1—
Mean-ATModel=MobileViT-XS2026.0640.44.420.76.821.62.617.35.711.3—
QVecQuantization level=INT82026.0639.7————————5.48
DATModel=ResNet-342026.0639.30.25.41324.66.511.812.310.5—
MNGModel=MobileViT-XS2026.0638.44.220.47.721.33.616.86.411.5—
GBNModel=ResNet-342026.0636.3003.915.41.20.93.73.6—
RAMPModel=MobileViT-XS2026.06329.913.30.880.414.20.96.8—
RAMPModel=ResNet-342026.0630.511.717.51.612.8118.529.3—
DATModel=MobileViT-XS2026.0627515.43.513.81.612.43.27.8—
PUATModel=ResNet-342026.0620.41.10.31.10.510.40.7—
GBNModel=MobileViT-XS2026.061.90000.50000.1—
PUATModel=MobileViT-XS2026.060.90.60.70.50.60.50.60.50.6—