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randomized smoothing method

To tackle this issue, we propose to employ the randomized smoothing method to verify the robustness of $K$-NN classifiers.

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

randomized smoothing method

We apply an implementable randomized smoothing method and propose a multistage scheme to progressively reduce the variance of the gradient estimator of the smoothed functions.

Randomized Smoothing Variance Reduction Method for Large-Scale Non-smooth Convex Optimization

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10.1109/tifs.2021.3116438

randomized smoothing, DefenseGAN, variational autoencoder (VAE), and Parallel WaveGAN vocoder (PWG) are compared against the baseline defense of adversarial training.

Study of Pre-Processing Defenses Against Adversarial Attacks on State-of-the-Art Speaker Recognition Systems

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10.1145/3442188.3445894

Further, we demonstrate that adversarial training and randomized smoothing techniques have opposite effects on leave-one-out fairness, which sheds light on the relationships between robustness, memorization, individual fairness, and leave-one-out fairness in deep models.

Leave-one-out Unfairness

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10.1360/SSI-2020-0172

To tackle this issue, we propose to employ the randomized smoothing method to verify the robustness of $K$-NN classifiers.

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

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10.1007/S43069-021-00059-Y

We apply an implementable randomized smoothing method and propose a multistage scheme to progressively reduce the variance of the gradient estimator of the smoothed functions.

Randomized Smoothing Variance Reduction Method for Large-Scale Non-smooth Convex Optimization

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10.1145/3447548.3467295

Our results are based on a recently proposed technique called randomized smoothing, which we extend to graph data.

Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation

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10.1145/3450569.3463560

Moreover, we generalize a randomized smoothing based certified defense to defend against our backdoor attacks.

Backdoor Attacks to Graph Neural Networks

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Randomized Smoothing - randomized smoothing, DefenseGAN, variational autoencoder (VAE), and Parallel WaveGAN vocoder (PWG) are compared against the baseline defense of adversarial training. [1] Further, we demonstrate that adversarial training and randomized smoothing techniques have opposite effects on leave-one-out fairness, which sheds light on the relationships between robustness, memorization, individual fairness, and leave-one-out fairness in deep models. [2] To tackle this issue, we propose to employ the randomized smoothing method to verify the robustness of $K$-NN classifiers. [3] We apply an implementable randomized smoothing method and propose a multistage scheme to progressively reduce the variance of the gradient estimator of the smoothed functions. [4] Our results are based on a recently proposed technique called randomized smoothing, which we extend to graph data. [5] Moreover, we generalize a randomized smoothing based certified defense to defend against our backdoor attacks. [6]

randomized smoothing method

To tackle this issue, we propose to employ the randomized smoothing method to verify the robustness of $K$-NN classifiers. [1] We apply an implementable randomized smoothing method and propose a multistage scheme to progressively reduce the variance of the gradient estimator of the smoothed functions. [2]