  In recent years, research on adversarial attacks has become a hot spot.
Although current literature on the transfer-based adversarial attack has
achieved promising results for improving the transferability to unseen
black-box models, it still leaves a long way to go. Inspired by the idea of
meta-learning, this paper proposes a novel architecture called Meta Gradient
Adversarial Attack (MGAA), which is plug-and-play and can be integrated with
any existing gradient-based attack method for improving the cross-model
transferability. Specifically, we randomly sample multiple models from a model
zoo to compose different tasks and iteratively simulate a white-box attack and
a black-box attack in each task. By narrowing the gap between the gradient
directions in white-box and black-box attacks, the transferability of
adversarial examples on the black-box setting can be improved. Extensive
experiments on the CIFAR10 and ImageNet datasets show that our architecture
outperforms the state-of-the-art methods for both black-box and white-box
attack settings.
