Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912314032128000 |
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| author | Wang, Zeyu Xie, Cihang Bartoldson, Brian Kailkhura, Bhavya |
| author_facet | Wang, Zeyu Xie, Cihang Bartoldson, Brian Kailkhura, Bhavya |
| contents | This paper investigates the robustness of vision-language models against adversarial visual perturbations and introduces a novel ``double visual defense" to enhance this robustness. Unlike previous approaches that resort to lightweight adversarial fine-tuning of a pre-trained CLIP model, we perform large-scale adversarial vision-language pre-training from scratch using web-scale data. We then strengthen the defense by incorporating adversarial visual instruction tuning. The resulting models from each stage, $Δ$CLIP and $Δ^2$LLaVA, show substantially enhanced zero-shot robustness and set a new state-of-the-art in adversarial defense for vision-language models. For example, the adversarial robustness of $Δ$CLIP surpasses that of the previous best models on ImageNet-1k by ~20%. %For example, $Δ$CLIP surpasses the previous best models on ImageNet-1k by ~20% in terms of adversarial robustness. Similarly, compared to prior art, $Δ^2$LLaVA brings a ~30% robustness improvement to image captioning task and a ~20% robustness improvement to visual question answering task. Furthermore, our models exhibit stronger zero-shot recognition capability, fewer hallucinations, and superior reasoning performance compared to baselines. Our project page is https://doublevisualdefense.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09446 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness Wang, Zeyu Xie, Cihang Bartoldson, Brian Kailkhura, Bhavya Computer Vision and Pattern Recognition This paper investigates the robustness of vision-language models against adversarial visual perturbations and introduces a novel ``double visual defense" to enhance this robustness. Unlike previous approaches that resort to lightweight adversarial fine-tuning of a pre-trained CLIP model, we perform large-scale adversarial vision-language pre-training from scratch using web-scale data. We then strengthen the defense by incorporating adversarial visual instruction tuning. The resulting models from each stage, $Δ$CLIP and $Δ^2$LLaVA, show substantially enhanced zero-shot robustness and set a new state-of-the-art in adversarial defense for vision-language models. For example, the adversarial robustness of $Δ$CLIP surpasses that of the previous best models on ImageNet-1k by ~20%. %For example, $Δ$CLIP surpasses the previous best models on ImageNet-1k by ~20% in terms of adversarial robustness. Similarly, compared to prior art, $Δ^2$LLaVA brings a ~30% robustness improvement to image captioning task and a ~20% robustness improvement to visual question answering task. Furthermore, our models exhibit stronger zero-shot recognition capability, fewer hallucinations, and superior reasoning performance compared to baselines. Our project page is https://doublevisualdefense.github.io/. |
| title | Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.09446 |