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training method\\attackwhite boxblack box
No Noise FGSPGDFGS(A)PGD(A')FGS(B'）PGD(B'))）FGS(C')PGD(C')
standard(A)99.40%23.70%0.00%39.49%3.41%90.56%86.10%94.21%91.36%
adversarial PGD(B)98.70%95.46%92.92%95.78%96.18%95.58%95.01%97.05%96.48%
adversarial network(C)99.32%94.66%87.09%95.75%96.19%96.15%95.24%96.96%95.78%
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training method\\attackwhite boxblack box
No Noise FGSPGDFGS(A')PGD(A')FGS(B’）PGD(B')FGS(C')]PGD(C')
standard(A)96.34%64.64%3.69%69.47% 49.92%56.46%44.25%89.71%83.02%
adversarial PGD(B)87.45%55.94%42.96%85.21%83.46% 59.09%48.20%87.41%83.23%
adversarial network(C)96.34%91.51%37.97%90.02%88.04%75.34%57.52%91.48%81.68%
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training method\\attackwhite boxblack box
No Noise FGSPGDFGS(A') PGD(A')FGS(B'） PGD(B') IFGS(C')PGD(C')
ensemble(MNIST,ε=0.3)98.65%2.52%0.00%90.91% 93.91%90.94%88.12%90.79%89.61%
adv. net(MNIST,ε=0.3)99.03%94.66%87.09%95.75% 96.19%96.15%95.24%96.96%95.78%
ensemble(SVHN,e=0.05)95.30%79.16%2.74%95.32% 93.88%67.97%54.81%95.60%93.21%
adv. net(SVHN,e=0.05)96.34%91.51%37.97%90.02% 88.04%75.34%57.52%91.48%81.68%
ensemble(CIFAR10,e= 887.17%57.91%11.35%85.01% 85.19%68.76%67.41%79.64%70.98%
adv. net(CIFAR10,ε= 2591.08%72.81%44.28%81.74%79.48% 77.23%74.04%78.51%66.74%
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training method\\attackwhite boxblack box
No Noise FGSPGDFGS(A') PGD(A')FGS(B'） PGD(B')FGS(C')PGD(C')
standard(A)91.59%57.31%1.32%67.99%22.88%77.13%75.06%73.34%55.34%
adversarial PGD(B)75.30%47.63%41.16%74.04%74.23%57.73%55.72%73.31%73.09%
adversarial network(C)91.08%72.81%44.28%81.74%79.48%77.23%74.04%78.51%66.74%
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training method\\attackwhite boxblack box
No NoiseFGSPGDFGS(A')PGD(A')FGS(B')PGD(B')
autoencoder(8 filters)88.70%67.28%33.56%78.94%74.15%70.70%68.54%
autoencoder(64 filters)89.10%67.05%33.38%79.56%74.40%70.52%68.66%
random Gaussian89.73%69.43%35.16%80.09%76.02%71.22%69.66%
label88.72%67.09%37.70%80.95%77.80%70.90%68.81%
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GeneratorOriginal (A)standard(A')adversarial PGD(B')adversarial network(C')
original accuracy91.59%90.54%75.71%89.21%
autoencoder(8 filters)31.07%41.40%74.56%84.59%
autoencoder(64 filters)6.08%14.74%75.52%86.64%
random Gaussian45.27%58.68%75.02%87.33%
label11.17%23.29%74.78%82.43%
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training method attackwhite boxblack box
No Noise FGSPGDFGS(A'） PGD(A'） FGS(B'） PGD(B') FGS(C') PGD(C')
standard(A)70.11%31.27%4.61%44.24%32.55%53.65%50.51%47.82%34.99%
adversarial PGD(B)55.53%32.13%28.48%53.91%54.62%41.80%40.61%54.55% 53.57%
adversarial network(C)70.99 %41.86%18.25%58.11%56.94% 53.15% 51.87%53.35%46.61%
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training method\\attackwhite boxblack box
No Noise FGSPGDFGS(A) PGD(A)]FGS(B'） PGD(B’） FGS(C')PGD(C')
standard(A)79.22%44.86%6.38%48.52% 13.42%65.18%63.56%53.17%28.04%
adversarial PGD(B)66.68%45.54%38.36%64.81% 64.84%53.41%49.77%64.35%62.44%
adversarial network(C)80.21%57.21%30.27%65.37% 58.27%67.38%64.28%61.11%40.27%
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training method\\attackwhite boxblack box
No Noise FGSPGDFGS(A)]）PGD(A'） FGS(B'）PGD(B'） FGS(C'） PGD(C")
standard(A)94.69%62.36%1.08%69.89% 9.14%85.05%82.82%76.27%45.16%
adversarial PGD(B)83.50%67.92%60.15%83.21% 82.96%72.66%68.82%82.01%78.59%
adversarial network(C)91.32%73.77%49.55%83.29 % 81.65%79.32%76.00%79.32%62.71%
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GeneratorOriginal (B)standard(A')adversarial PGD(B')adversarial network(C')
original accuracy75.30%90.54%75.71%89.21%
autoencoder(8 filters)72.74%88.38%73.07%87.89%
autoencoder(64 filters)71.26%87.64%71.98%87.02%
random Gaussian74.13%88.71%74.70%88.45%
label70.15%84.95%70.10%84.96%
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GeneratorOriginal (C)standard(A')adversarial PGD(B')adversarial network(C')
original accuracy91.08%90.54%75.71%89.21%
autoencoder(8 filters)74.66%66.95%74.42%80.55%
autoencoder(64 filters)53.68%46.37%73.08%72.18%
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