VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866910318895038464 |
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| author | Yin, Ziyi Ye, Muchao Zhang, Tianrong Du, Tianyu Zhu, Jinguo Liu, Han Chen, Jinghui Wang, Ting Ma, Fenglong |
| author_facet | Yin, Ziyi Ye, Muchao Zhang, Tianrong Du, Tianyu Zhu, Jinguo Liu, Han Chen, Jinghui Wang, Ting Ma, Fenglong |
| contents | Vision-Language (VL) pre-trained models have shown their superiority on many multimodal tasks. However, the adversarial robustness of such models has not been fully explored. Existing approaches mainly focus on exploring the adversarial robustness under the white-box setting, which is unrealistic. In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks. Towards this end, we propose VLATTACK to generate adversarial samples by fusing perturbations of images and texts from both single-modal and multimodal levels. At the single-modal level, we propose a new block-wise similarity attack (BSA) strategy to learn image perturbations for disrupting universal representations. Besides, we adopt an existing text attack strategy to generate text perturbations independent of the image-modal attack. At the multimodal level, we design a novel iterative cross-search attack (ICSA) method to update adversarial image-text pairs periodically, starting with the outputs from the single-modal level. We conduct extensive experiments to attack five widely-used VL pre-trained models for six tasks. Experimental results show that VLATTACK achieves the highest attack success rates on all tasks compared with state-of-the-art baselines, which reveals a blind spot in the deployment of pre-trained VL models. Source codes can be found at https://github.com/ericyinyzy/VLAttack. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_04655 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models Yin, Ziyi Ye, Muchao Zhang, Tianrong Du, Tianyu Zhu, Jinguo Liu, Han Chen, Jinghui Wang, Ting Ma, Fenglong Cryptography and Security Computer Vision and Pattern Recognition Vision-Language (VL) pre-trained models have shown their superiority on many multimodal tasks. However, the adversarial robustness of such models has not been fully explored. Existing approaches mainly focus on exploring the adversarial robustness under the white-box setting, which is unrealistic. In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks. Towards this end, we propose VLATTACK to generate adversarial samples by fusing perturbations of images and texts from both single-modal and multimodal levels. At the single-modal level, we propose a new block-wise similarity attack (BSA) strategy to learn image perturbations for disrupting universal representations. Besides, we adopt an existing text attack strategy to generate text perturbations independent of the image-modal attack. At the multimodal level, we design a novel iterative cross-search attack (ICSA) method to update adversarial image-text pairs periodically, starting with the outputs from the single-modal level. We conduct extensive experiments to attack five widely-used VL pre-trained models for six tasks. Experimental results show that VLATTACK achieves the highest attack success rates on all tasks compared with state-of-the-art baselines, which reveals a blind spot in the deployment of pre-trained VL models. Source codes can be found at https://github.com/ericyinyzy/VLAttack. |
| title | VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models |
| topic | Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.04655 |