  Substitute model black box attacks can create adversarial examples for a
target model just by accessing its output labels. This poses a major challenge
to machine learning models in practice, particularly in security sensitive
applications. The 01 loss model is known to be more robust to outliers and
noise than convex models that are typically used in practice. Motivated by
these properties we present 01 loss linear and 01 loss dual layer neural
network models as a defense against transfer based substitute model black box
attacks. We compare the accuracy of adversarial examples from substitute model
black box attacks targeting our 01 loss models and their convex counterparts
for binary classification on popular image benchmarks. Our 01 loss dual layer
neural network has an adversarial accuracy of 66.2%, 58%, 60.5%, and 57% on
MNIST, CIFAR10, STL10, and ImageNet respectively whereas the sigmoid activated
logistic loss counterpart has accuracies of 63.5%, 19.3%, 14.9%, and 27.6%.
Except for MNIST the convex counterparts have substantially lower adversarial
accuracies. We show practical applications of our models to deter traffic sign
and facial recognition adversarial attacks. On GTSRB street sign and CelebA
facial detection our 01 loss network has 34.6% and 37.1% adversarial accuracy
respectively whereas the convex logistic counterpart has accuracy 24% and 1.9%.
Finally we show that our 01 loss network can attain robustness on par with
simple convolutional neural networks and much higher than its convex
counterpart even when attacked with a convolutional network substitute model.
Our work shows that 01 loss models offer a powerful defense against substitute
model black box attacks.
