  Recently, transformer architecture has demonstrated its significance in both
Natural Language Processing (NLP) and Computer Vision (CV) tasks. Though other
network models are known to be vulnerable to the backdoor attack, which embeds
triggers in the model and controls the model behavior when the triggers are
presented, little is known whether such an attack is still valid on the
transformer models and if so, whether it can be done in a more cost-efficient
manner. In this paper, we propose DBIA, a novel data-free backdoor attack
against the CV-oriented transformer networks, leveraging the inherent attention
mechanism of transformers to generate triggers and injecting the backdoor using
the poisoned surrogate dataset. We conducted extensive experiments based on
three benchmark transformers, i.e., ViT, DeiT and Swin Transformer, on two
mainstream image classification tasks, i.e., CIFAR10 and ImageNet. The
evaluation results demonstrate that, consuming fewer resources, our approach
can embed backdoors with a high success rate and a low impact on the
performance of the victim transformers. Our code is available at
https://anonymous.4open.science/r/DBIA-825D.
