Defending against substitute model black box adversarial attacks with the 01 loss
This addresses security challenges in machine learning applications such as traffic sign and facial recognition, though it is incremental as it builds on known robustness properties of 01 loss.
The paper tackles the problem of defending against substitute model black-box adversarial attacks by proposing 01 loss linear and dual-layer neural network models, which achieve higher adversarial accuracies than convex counterparts on benchmarks like CIFAR10 (58% vs. 19.3%) and ImageNet (57% vs. 27.6%).
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.