  Deep neural networks (DNNs) are vulnerable to adversarial examples. And, the
adversarial examples have transferability, which means that an adversarial
example for a DNN model can fool another model with a non-trivial probability.
This gave birth to the transfer-based attack where the adversarial examples
generated by a surrogate model are used to conduct black-box attacks. There are
some work on generating the adversarial examples from a given surrogate model
with better transferability. However, training a special surrogate model to
generate adversarial examples with better transferability is relatively
under-explored. This paper proposes a method for training a surrogate model
with dark knowledge to boost the transferability of the adversarial examples
generated by the surrogate model. This trained surrogate model is named dark
surrogate model (DSM). The proposed method for training a DSM consists of two
key components: a teacher model extracting dark knowledge, and the mixing
augmentation skill enhancing dark knowledge of training data. We conducted
extensive experiments to show that the proposed method can substantially
improve the adversarial transferability of surrogate models across different
architectures of surrogate models and optimizers for generating adversarial
examples, and it can be applied to other scenarios of transfer-based attack
that contain dark knowledge, like face verification. Our code is publicly
available at \url{https://github.com/ydc123/Dark_Surrogate_Model}.
