  ImageNet pre-training has enabled state-of-the-art results on many tasks. In
spite of its recognized contribution to generalization, we observed in this
study that ImageNet pre-training also transfers adversarial non-robustness from
pre-trained model into fine-tuned model in the downstream classification tasks.
We first conducted experiments on various datasets and network backbones to
uncover the adversarial non-robustness in fine-tuned model. Further analysis
was conducted on examining the learned knowledge of fine-tuned model and
standard model, and revealed that the reason leading to the non-robustness is
the non-robust features transferred from ImageNet pre-trained model. Finally,
we analyzed the preference for feature learning of the pre-trained model,
explored the factors influencing robustness, and introduced a simple robust
ImageNet pre-training solution. Our code is available at
\url{https://github.com/jiamingzhang94/ImageNet-Pretraining-transfers-non-robustness}.
