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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.13507 |
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| _version_ | 1866909144421761024 |
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| author | Li, Jinmin Gao, Kuofeng Bai, Yang Zhang, Jingyun Xia, Shu-tao Wang, Yisen |
| author_facet | Li, Jinmin Gao, Kuofeng Bai, Yang Zhang, Jingyun Xia, Shu-tao Wang, Yisen |
| contents | Despite the remarkable performance of video-based large language models (LLMs), their adversarial threat remains unexplored. To fill this gap, we propose the first adversarial attack tailored for video-based LLMs by crafting flow-based multi-modal adversarial perturbations on a small fraction of frames within a video, dubbed FMM-Attack. Extensive experiments show that our attack can effectively induce video-based LLMs to generate incorrect answers when videos are added with imperceptible adversarial perturbations. Intriguingly, our FMM-Attack can also induce garbling in the model output, prompting video-based LLMs to hallucinate. Overall, our observations inspire a further understanding of multi-modal robustness and safety-related feature alignment across different modalities, which is of great importance for various large multi-modal models. Our code is available at https://github.com/THU-Kingmin/FMM-Attack. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13507 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMs Li, Jinmin Gao, Kuofeng Bai, Yang Zhang, Jingyun Xia, Shu-tao Wang, Yisen Computer Vision and Pattern Recognition Despite the remarkable performance of video-based large language models (LLMs), their adversarial threat remains unexplored. To fill this gap, we propose the first adversarial attack tailored for video-based LLMs by crafting flow-based multi-modal adversarial perturbations on a small fraction of frames within a video, dubbed FMM-Attack. Extensive experiments show that our attack can effectively induce video-based LLMs to generate incorrect answers when videos are added with imperceptible adversarial perturbations. Intriguingly, our FMM-Attack can also induce garbling in the model output, prompting video-based LLMs to hallucinate. Overall, our observations inspire a further understanding of multi-modal robustness and safety-related feature alignment across different modalities, which is of great importance for various large multi-modal models. Our code is available at https://github.com/THU-Kingmin/FMM-Attack. |
| title | FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.13507 |