  In the last decade, deep neural networks have proven to be very powerful in
computer vision tasks, starting a revolution in the computer vision and machine
learning fields. However, deep neural networks, usually, are not robust to
perturbations of the input data. In fact, several studies showed that slightly
changing the content of the images can cause a dramatic decrease in the
accuracy of the attacked neural network. Several methods able to generate
adversarial samples make use of gradients, which usually are not available to
an attacker in real-world scenarios. As opposed to this class of attacks,
another class of adversarial attacks, called black-box adversarial attacks,
emerged, which does not make use of information on the gradients, being more
suitable for real-world attack scenarios. In this work, we compare three
well-known evolution strategies on the generation of black-box adversarial
attacks for image classification tasks. While our results show that the
attacked neural networks can be, in most cases, easily fooled by all the
algorithms under comparison, they also show that some black-box optimization
algorithms may be better in "harder" setups, both in terms of attack success
rate and efficiency (i.e., number of queries).

Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing