Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness

5 Dec 2021  ·  Konstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis ·

This work explores the potency of stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA), against powerful (gradient-based) white-box and black-box adversarial attacks; we especially focus on Adversarial Training settings. In our work, we replace the conventional ReLU-based nonlinearities with blocks comprising locally and stochastically competing linear units. The output of each network layer now yields a sparse output, depending on the outcome of winner sampling in each block. We rely on the Variational Bayesian framework for training and inference; we incorporate conventional PGD-based adversarial training arguments to increase the overall adversarial robustness. As we experimentally show, the arising networks yield state-of-the-art robustness against powerful adversarial attacks while retaining very high classification rate in the benign case.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Adversarial Robustness CIFAR-10 Stochastic-LWTA/PGD/WideResNet-34-10 Attack: AutoAttack 82.6 # 1
Accuracy 92.26 # 2
Robust Accuracy 84.3 # 1
Adversarial Defense CIFAR-10 Stochastic-LWTA/PGD/WideResNet-34-10 Accuracy 84.3 # 2
Attack: AutoAttack 82.6 # 1
Adversarial Robustness CIFAR-10 Stochastic-LWTA/PGD/WideResNet-34-5 Attack: AutoAttack 81.22 # 2
Accuracy 91.88 # 3
Robust Accuracy 83.4 # 2
Adversarial Defense CIFAR-10 Stochastic-LWTA/PGD/WideResNet-34-5 Attack: AutoAttack 81.22 # 2
Adversarial Defense CIFAR-10 Ours (Stochastic-LWTA/PGD/WideResNet-34-5) Accuracy 83.4 # 3
Adversarial Defense CIFAR-10 Ours (Stochastic-LWTA/PGD/WideResNet-34-1) Accuracy 81.87 # 4
Attack: AutoAttack 74.71 # 3


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