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Targeted Adversarial Attack on ImageNet-Compatible

89Avg Success RateRaPA
-3.45620.54744.5568.553May 24, 2023Sep 17, 2023Jan 12, 2024May 8, 2024Sep 2, 2024Dec 28, 2024Apr 24, 2025

Evaluation results

MethodLinks
RaPASurrogate Model=RN50, Combined with=MI-TI2025.048998.2—————————939182.688.593.690.982.185.585
CFMSurrogate Model=RN50, Combined with=MI-TI2025.0486.598—————————9289.574.885.693.485.282.682.581.7
RaPASurrogate Model=DN121, Combined with=MI-TI2025.048690.2—————————89.685.177.884.497.484.483.284.183.7
MUPSurrogate Model=RN50, Combined with=MI-TI2025.0479.397.7—————————90.385.661.87591.679.370.971.868.7
CFMSurrogate Model=DN121, Combined with=MI-TI2025.0475.688.9—————————82.478.660.969.998.560.57372.770.3
ODISurrogate Model=RN50, Combined with=MI-TI2025.0475.598.9—————————77.58363.969.989.861.27171.169.2
BSRSurrogate Model=RN50, Combined with=MI-TI2025.0475.198.5—————————87.984.154.97091.665.366.568.963.3
AdmixSurrogate Model=RN50, Combined with=MI-TI2025.0473.298.1—————————83.278.157.167.886.870.361.964.464.5
SISurrogate Model=RN50, Combined with=MI-TI2025.0472.898.8—————————82.973.760.8669059.466.968.760.6
MUPSurrogate Model=DN121, Combined with=MI-TI2025.0469.586.9—————————79.975.250.360.597.751.465.766.561
SIASurrogate Model=RN50, Combined with=MI-TI2025.0467.798.3—————————84.579.33967.188.470.14653.450.9
RaPASource Model=MambaVision-T2025.0463.8———————64.989.7——————————
AdmixSurrogate Model=DN121, Combined with=MI-TI2025.046374.3—————————72.465.846.955.197.951.255.556.653.9
RDISurrogate Model=RN50, Combined with=MI-TI2025.0461.798.7—————————80.675.432.852.687.449.647.851.140.7
ODISurrogate Model=DN121, Combined with=MI-TI2025.0460.671.5—————————59.669.648.152.298.935.558.16052.7
CFM-RDISource model=adv-RN-502023.0558.698.329.569.841.852.759.83.74————————————
SIASurrogate Model=DN121, Combined with=MI-TI2025.0458.479.3—————————69.266.234.954.898.145.140.651.644.1
ODISource model=adv-RN-502023.0551.897.322.257.738.84054.99.04————————————
BSRSurrogate Model=DN121, Combined with=MI-TI2025.0451.870.3—————————61.657.334.141.798.330.242.744.537.5
SI-RDISource model=adv-RN-502023.0549.798.719.457.635.335.252.116.34————————————
SISurrogate Model=DN121, Combined with=MI-TI2025.0447.960.9—————————54.84335.637.998.626.743.643.534.6
RaPASource Model=ConvNeXtV2-L2025.0446.4———————56.7100——————————
RaPASurrogate Model=ResNet-50, Optimization=MI-TI2025.0445—33.875.427.659.557.3———15.6—————————
Admix-RDISource model=adv-RN-502023.0543.598.912.155.523.132.438.99.86————————————
DISurrogate Model=RN50, Combined with=MI-TI2025.0441.598.7—————————60.164.68.128.677.328.413.521.115.1
RaPASource Model=DINOv2-Large2025.0441.4———————49.398.7——————————
RDISource model=adv-RN-502023.0540.798.810.849.519.929.435.83.29————————————
RaPASurrogate Model=DenseNet-121, Optimization=MI-TI2025.0440.3—27.869.423.553.154———14.1—————————
RDISurrogate Model=DN121, Combined with=MI-TI2025.0440.254.4—————————50.946.719.326.998.620.629.830.524.7
VT-RDISource model=adv-RN-502023.0539.598.510.646.32027.134.419.83————————————
FTMSurrogate Model=ResNet-50, Optimization=MI-TI2025.0433.3—1867.616.547.141.5———9.3—————————
CFMSurrogate Model=ResNet-50, Optimization=MI-TI2025.0432.2—17.365.814.647.539.9———7.9—————————
DISource model=adv-RN-502023.0531.998.95.736.910.119.220.53.77————————————
CFM-RDISource model=RN-502023.0530.875.54.346.18.925.224.73.72————————————
ODISource model=RN-502023.0527.864.75.13710.720.129.19.05————————————
DISurrogate Model=DN121, Combined with=MI-TI2025.0426.436.4—————————28.135.86.514.59910.19.813.910.1
ODISurrogate Model=ResNet-50, Optimization=MI-TI2025.0424.8—15.549.311.831.635.1———5.6—————————
FTMSurrogate Model=DenseNet-121, Optimization=MI-TI2025.0422.8—10.750.49.431.130.4———4.8—————————
CFMSurrogate Model=DenseNet-121, Optimization=MI-TI2025.0422.3—10.4498.330.130.4———5.5—————————
SI-RDISource model=RN-502023.052259.92.929.46.315.517.916.16————————————
VT-RDISource model=RN-502023.0521.664.22.928.15.2151419.83————————————
MUPSurrogate Model=ResNet-50, Optimization=MI-TI2025.0420.7—748.38.230.726.5———3.6—————————
CFMSource Model=ConvNeXtV2-L2025.0420.6———————28.4100——————————
CFMSource Model=DINOv2-Large2025.0420———————27.7100——————————
SISurrogate Model=ResNet-50, Optimization=MI-TI2025.0418.4—842.97.725.123.3———3.7—————————
BSRSurrogate Model=ResNet-50, Optimization=MI-TI2025.0417.7—6.842.47.725.321.9———2.3—————————
ODISurrogate Model=DenseNet-121, Optimization=MI-TI2025.0417.4—8.536.3—19.826.7———4.9—————————
AdmixSurrogate Model=ResNet-50, Optimization=MI-TI2025.0417.1—7.243.45.922.719.4———4.2—————————
MUPSurrogate Model=DenseNet-121, Optimization=MI-TI2025.0416.1—5.238.9523.220.1———4.1—————————
Admix-RDISource model=RN-502023.0515.952.41.322.52.58.58.49.73————————————
AdmixSurrogate Model=DenseNet-121, Optimization=MI-TI2025.0413.8—4.233.841819.1———3.9—————————
CFMSource Model=MambaVision-T2025.0412.5———————40.291.9——————————
DWPSurrogate Model=ResNet-50, Optimization=MI-TI2025.0412.1—3.732417.613.3———1.8—————————
RaPASurrogate Model=CLIP, Combined with=MI-TI2025.0411.69.5—————————8.84.914.617.514.98.512.512.812.4
SIASurrogate Model=ResNet-50, Optimization=MI-TI2025.0411.2—3.128.43.916.713.5———1.9—————————
RDISource model=RN-502023.0510.534.80.713.11.95.96.83.29————————————
ODISurrogate Model=CLIP, Combined with=MI-TI2025.049.97—————————7.75.11113.7136.511.311.811.5
SISurrogate Model=DenseNet-121, Optimization=MI-TI2025.049.4—3.921.54.410.214.3———2.1—————————
RDISurrogate Model=ResNet-50, Optimization=MI-TI2025.049.2—2.8244.412.610.1———1.2—————————
DWPSurrogate Model=DenseNet-121, Optimization=MI-TI2025.049.2—2.721.12.914.312———2.4—————————
SIASurrogate Model=DenseNet-121, Optimization=MI-TI2025.049—2.424.22.212.911.1———1.5—————————
BSRSurrogate Model=DenseNet-121, Optimization=MI-TI2025.048.5—2.721.62.710.611.9———1.2—————————
RDISurrogate Model=DenseNet-121, Optimization=MI-TI2025.045.3—1122.16.98.2———1.4—————————
CFMSurrogate Model=CLIP, Combined with=MI-TI2025.044.23.3—————————3.11.44.97.84.32.55.44.14.8
AdmixSurrogate Model=CLIP, Combined with=MI-TI2025.043.32.2—————————2.71.64.163.52.82.93.83.6
DISource model=RN-502023.052.910.90.13.60.31.31.53.73————————————
DISurrogate Model=ResNet-50, Optimization=MI-TI2025.042.3—0.46.70.63.81.8———0.5—————————
BSRSurrogate Model=CLIP, Combined with=MI-TI2025.0421.6—————————1.70.91.93.52.41.51.92.32.2
DISurrogate Model=DenseNet-121, Optimization=MI-TI2025.041.7—0.341.222.1———0.3—————————
SIASurrogate Model=CLIP, Combined with=MI-TI2025.041.71—————————0.90.81.83.51.61.91.31.82.6
SISurrogate Model=CLIP, Combined with=MI-TI2025.0410.5—————————0.60.61.41.61.4110.81.2
RDISurrogate Model=CLIP, Combined with=MI-TI2025.040.40.4—————————0.50.20.40.90.40.30.40.20.7
DISurrogate Model=CLIP, Combined with=MI-TI2025.040.10.1—————————0.10.1000.10.2000.1