SCIENTIA SINICA Informationis, Volume 51 , Issue 1 : 27(2021) https://doi.org/10.1360/SSI-2020-0172

Robustness verification of $\boldsymbol~K$-NN classifiers via constraint relaxation and randomized smoothing

• AcceptedAug 7, 2020
• PublishedDec 21, 2020
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References

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• Figure 1

(Color online) Curves of certified robust errors

• Figure 2

(a) Cumulative distribution function and (b) percent point function of the standard normal distribution

• Figure 3

(Color online) Comparison between $K$-NN and neural networks for robustness verification. (a) MNIST; protectłinebreak (b) Fashion-MNIST

• Table 1   Certified robust errors on MNIST
 Radius $\ell_2$ Constraint relaxation ($K$-NN) Random smoothing ($K$-NN) Random smoothing (neural network) 0 3.3 8.2 8.3 1 29.3 24.4 30.9 2 83.3 54.4 73.2 3 99.6 89.9 97.6
• Table 2   Certified robust errors on Fashion-MNIST
 Radius $\ell_2$ Constraint relaxation ($K$-NN) Random smoothing ($K$-NN) Random smoothing (neural network) 0 14.5 19.6 26.4 1 63.0 39.2 44.9 2 89.3 63.6 72.1 3 98.0 84.8 91.5

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