VQAttack: Transferable Adversarial Attacks on Visual Question Answering
  via Pre-trained Models
Ziyi Yin Muchao Ye Tianrong Zhang Jiaqi Wang Han Liu Jinghui Chen Ting Wang Fenglong Ma
Visual Question Answering (VQA) is a fundamental task in computer vision and
natural language process fields. Although the ``pre-training & finetuning''
learning paradigm significantly improves the VQA performance, the adversarial
robustness of such a learning paradigm has not been explored. In this paper, we
delve into a new problem: using a pre-trained multimodal source model to create
adversarial image-text pairs and then transferring them to attack the target
VQA models. Correspondingly, we propose a novel VQAttack model, which can
iteratively generate both image and text perturbations with the designed
modules: the large language model (LLM)-enhanced image attack and the
cross-modal joint attack module. At each iteration, the LLM-enhanced image
attack module first optimizes the latent representation-based loss to generate
feature-level image perturbations. Then it incorporates an LLM to further
enhance the image perturbations by optimizing the designed masked answer
anti-recovery loss. The cross-modal joint attack module will be triggered at a
specific iteration, which updates the image and text perturbations
sequentially. Notably, the text perturbation updates are based on both the
learned gradients in the word embedding space and word synonym-based
substitution. Experimental results on two VQA datasets with five validated
models demonstrate the effectiveness of the proposed VQAttack in the
transferable attack setting, compared with state-of-the-art baselines. This
work reveals a significant blind spot in the ``pre-training & fine-tuning''
paradigm on VQA tasks. Source codes will be released.