Title: Exploring the Space of Black-box Attacks on Deep Neural Networks,
Abstract: Existing black-box attacks on deep neural networks (DNNs) so far have largely
focused on transferability, where an adversarial instance generated for a
locally trained model can "transfer" to attack other learning models. In this
paper, we propose novel Gradient Estimation black-box attacks for adversaries
with query access to the target model's class probabilities, which do not rely
on transferability. We also propose strategies to decouple the number of
queries required to generate each adversarial sample from the dimensionality of
the input. An iterative variant of our attack achieves close to 100%
adversarial success rates for both targeted and untargeted attacks on DNNs. We
carry out extensive experiments for a thorough comparative evaluation of
black-box attacks and show that the proposed Gradient Estimation attacks
outperform all transferability based black-box attacks we tested on both MNIST
and CIFAR-10 datasets, achieving adversarial success rates similar to well
known, state-of-the-art white-box attacks. We also apply the Gradient
Estimation attacks successfully against a real-world Content Moderation
classifier hosted by Clarifai. Furthermore, we evaluate black-box attacks
against state-of-the-art defenses. We show that the Gradient Estimation attacks
are very effective even against these defenses.