  Modern deep neural networks are often vulnerable to adversarial samples.
Based on the first optimization-based attacking method, many following methods
are proposed to improve the attacking performance and speed. Recently,
generation-based methods have received much attention since they directly use
feed-forward networks to generate the adversarial samples, which avoid the
time-consuming iterative attacking procedure in optimization-based and
gradient-based methods. However, current generation-based methods are only able
to attack one specific target (category) within one model, thus making them not
applicable to real classification systems that often have hundreds/thousands of
categories. In this paper, we propose the first Multi-target Adversarial
Network (MAN), which can generate multi-target adversarial samples with a
single model. By incorporating the specified category information into the
intermediate features, it can attack any category of the target classification
model during runtime. Experiments show that the proposed MAN can produce
stronger attack results and also have better transferability than previous
state-of-the-art methods in both multi-target attack task and single-target
attack task. We further use the adversarial samples generated by our MAN to
improve the robustness of the classification model. It can also achieve better
classification accuracy than other methods when attacked by various methods.
