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Found 5 papers, 4 papers with code
Defending against black-box adversarial attacks with gradient-free trained sign activation neural networks
The non-transferability in our ensemble also makes it a powerful defense to substitute model black box attacks that we show require a much greater distortion than binary and full precision networks to bring our model to zero adversarial accuracy.
Towards adversarial robustness with 01 loss neural networks
To further validate these results we subject all models to substitute model black box attacks under different distortion thresholds and find that the 01 loss network is the hardest to attack across all distortions.
On the transferability of adversarial examples between convex and 01 loss models
Indeed we see on MNIST that adversaries transfer between 01 loss and convex models more easily than on CIFAR10 and ImageNet which are likely to contain outliers.
Robust binary classification with the 01 loss
We show our algorithms to be fast and comparable in accuracy to the linear support vector machine and logistic loss single hidden layer network for binary classification on several image benchmarks, thus establishing that our method is on-par in test accuracy with convex losses.
A fully 3D multi-path convolutional neural network with feature fusion and feature weighting for automatic lesion identification in brain MRI images
We propose a fully 3D multi-path convolutional network to predict stroke lesions from 3D brain MRI images.
