  State-of-the-art deep neural networks are known to be vulnerable to
adversarial examples, formed by applying small but malicious perturbations to
the original inputs. Moreover, the perturbations can \textit{transfer across
models}: adversarial examples generated for a specific model will often mislead
other unseen models. Consequently the adversary can leverage it to attack
deployed systems without any query, which severely hinder the application of
deep learning, especially in the areas where security is crucial. In this work,
we systematically study how two classes of factors that might influence the
transferability of adversarial examples. One is about model-specific factors,
including network architecture, model capacity and test accuracy. The other is
the local smoothness of loss function for constructing adversarial examples.
Based on these understanding, a simple but effective strategy is proposed to
enhance transferability. We call it variance-reduced attack, since it utilizes
the variance-reduced gradient to generate adversarial example. The
effectiveness is confirmed by a variety of experiments on both CIFAR-10 and
ImageNet datasets.
