  Face recognition has achieved great success in the last five years due to the
development of deep learning methods. However, deep convolutional neural
networks (DCNNs) have been found to be vulnerable to adversarial examples. In
particular, the existence of transferable adversarial examples can severely
hinder the robustness of DCNNs since this type of attacks can be applied in a
fully black-box manner without queries on the target system. In this work, we
first investigate the characteristics of transferable adversarial attacks in
face recognition by showing the superiority of feature-level methods over
label-level methods. Then, to further improve transferability of feature-level
adversarial examples, we propose DFANet, a dropout-based method used in
convolutional layers, which can increase the diversity of surrogate models and
obtain ensemble-like effects. Extensive experiments on state-of-the-art face
models with various training databases, loss functions and network
architectures show that the proposed method can significantly enhance the
transferability of existing attack methods. Finally, by applying DFANet to the
LFW database, we generate a new set of adversarial face pairs that can
successfully attack four commercial APIs without any queries. This TALFW
database is available to facilitate research on the robustness and defense of
deep face recognition.
