Title: Exploring and Enhancing the Transferability of Adversarial Examples. Abstract:  State-of-the-art deep neural networks are 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 hinders the application of deep learning, especially in the safety-critical areas. In this work, we empirically study how two classes of factors those 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 surface for constructing adversarial examples.  Inspired by  these understandings on the transferability of adversarial examples,  we then propose a simple but effective strategy  to enhance the transferability, whose effectiveness is confirmed by a variety of experiments on both  CIFAR-10 and ImageNet datasets.