  This paper focuses on an important type of black-box attacks, i.e.,
transfer-based adversarial attacks, where the adversary generates adversarial
examples by a substitute (source) model and utilize them to attack an unseen
target model, without knowing its information. Existing methods tend to give
unsatisfactory adversarial transferability when the source and target models
are from different types of DNN architectures (e.g. ResNet-18 and Swin
Transformer). In this paper, we observe that the above phenomenon is induced by
the output inconsistency problem. To alleviate this problem while effectively
utilizing the existing DNN models, we propose a common knowledge learning (CKL)
framework to learn better network weights to generate adversarial examples with
better transferability, under fixed network architectures. Specifically, to
reduce the model-specific features and obtain better output distributions, we
construct a multi-teacher framework, where the knowledge is distilled from
different teacher architectures into one student network. By considering that
the gradient of input is usually utilized to generated adversarial examples, we
impose constraints on the gradients between the student and teacher models, to
further alleviate the output inconsistency problem and enhance the adversarial
transferability. Extensive experiments demonstrate that our proposed work can
significantly improve the adversarial transferability.
