Adversarial Attack on ImageNet 1k 1000 images (Model Robustness) benchmark leaderboard · SOTA2 Research

Adversarial Attack on ImageNet 1k 1000 images (Model Robustness)

100Robust Accuracy (Inc-v3)BIM

Evaluation Results

Method	Links
BIM2022.09		100	20.3	15.7	15.6	34.3	21.5
SIM2022.09		100	38.2	31.1	35.9	42.2	36.9
DIM2022.09		100	31.7	25.5	31.4	45.5	33.5
NI-FGSM2022.09		100	40	35.2	39.9	56.9	43
MI-FGSM2022.09		100	40.2	35.1	40.3	57.1	43.2
VMI-FGSM2022.09		100	63	59.3	68.6	70.3	65.3
VNI-FGSM2022.09		100	62.4	58.7	67.7	69.7	64.6
ANDA2022.09		100	76.1	72.8	82.3	77	77.1
MultiANDA2022.09		100	79.2	76	84.5	78.8	79.6
TAIG2022.09		99.7	53.3	45.9	56.7	54.2	52.5
FIA2022.09		98.3	78.4	75.3	81.2	83.5	79.6
MultiANDA2022.09		93.9	87.1	85.6	99.8	84.3	87.7
ANDA2022.09		93	86.4	83.7	99.8	82.8	86.5
FIA2022.09		82.2	75.3	72.4	89.2	80.7	77.7
VMI-FGSM2022.09		81.1	69.6	66.4	99.3	73.5	72.7
VNI-FGSM2022.09		80.9	70	65.8	99.4	73.8	72.6
TAIG2022.09		73.9	63.4	58.4	95	57.4	63.3
TIM2022.09		64.3	35.9	30.6	25.4	70.4	45.3
NI-FGSM2022.09		61.9	49.5	44.7	99.2	64.7	55.2
MI-FGSM2022.09		60.3	49.3	43	98.8	64.6	54.3
SIM2022.09		58.9	47.8	41.4	99.6	49.8	49.5
DIM2022.09		54.6	41.4	36.6	98.2	50	45.7
TIM2022.09		43.4	36.5	32	36	66.2	42.8
BIM2022.09		36.3	25.6	20.6	99.3	37.8	30.1