  Deep neural networks (DNNs) are sensitive to adversarial data in a variety of
scenarios, including the black-box scenario, where the attacker is only allowed
to query the trained model and receive an output. Existing black-box methods
for creating adversarial instances are costly, often using gradient estimation
or training a replacement network. This paper introduces
\textbf{Qu}ery-Efficient \textbf{E}volutiona\textbf{ry} \textbf{Attack},
\textit{QuEry Attack}, an untargeted, score-based, black-box attack. QuEry
Attack is based on a novel objective function that can be used in gradient-free
optimization problems. The attack only requires access to the output logits of
the classifier and is thus not affected by gradient masking. No additional
information is needed, rendering our method more suitable to real-life
situations. We test its performance with three different state-of-the-art
models -- Inception-v3, ResNet-50, and VGG-16-BN -- against three benchmark
datasets: MNIST, CIFAR10 and ImageNet. Furthermore, we evaluate QuEry Attack's
performance on non-differential transformation defenses and state-of-the-art
robust models. Our results demonstrate the superior performance of QuEry
Attack, both in terms of accuracy score and query efficiency.

Computer Vision and Pattern Recognition;Neural and Evolutionary Computing