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 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 VOAttack in the transferable attack setting, compared with state-of-the-art baselines. This work also reveals a significant blind spot in the ``pre-training \& fine-tuning'' paradigm on VQA tasks.

VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models

Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domain, existing pretrained models on electronic health records (EHR) fail to capture the hierarchical nature of EHR data, limiting their generalization capability across diverse downstream tasks using a single pretrained model.
To tackle this challenge, this paper introduces a novel, general, and unified pretraining framework called MedHMP, specifically designed for hierarchically multimodal EHR data. The effectiveness of the proposed MedHMP is demonstrated through experimental results on eight downstream tasks spanning three levels. Comparisons against eighteen baselines further highlight the efficacy of our approach.

Hierarchical Pretraining on Multimodal Electronic Health Records

Main Track - Computer Vision

technical paper

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VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models

Hierarchical Pretraining on Multimodal Electronic Health Records

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