  While vision-language pre-training model (VLP) has shown revolutionary
improvements on various vision-language (V+L) tasks, the studies regarding its
adversarial robustness remain largely unexplored. This paper studied the
adversarial attack on popular VLP models and V+L tasks. First, we analyzed the
performance of adversarial attacks under different settings. By examining the
influence of different perturbed objects and attack targets, we concluded some
key observations as guidance on both designing strong multimodal adversarial
attack and constructing robust VLP models. Second, we proposed a novel
multimodal attack method on the VLP models called Collaborative Multimodal
Adversarial Attack (Co-Attack), which collectively carries out the attacks on
the image modality and the text modality. Experimental results demonstrated
that the proposed method achieves improved attack performances on different V+L
downstream tasks and VLP models. The analysis observations and novel attack
method hopefully provide new understanding into the adversarial robustness of
VLP models, so as to contribute their safe and reliable deployment in more
real-world scenarios. Code is available at
https://github.com/adversarial-for-goodness/Co-Attack.
