CBV: Clean-label Backdoor Attacks on Vision Language Models via Diffusion Models
📰 ArXiv cs.AI
Learn to defend Vision-Language Models against clean-label backdoor attacks using diffusion models and understand the vulnerability of VLMs to such attacks
Action Steps
- Apply diffusion models to generate poisoned samples for backdoor attacks on VLMs
- Configure VLMs to be vulnerable to clean-label backdoor attacks
- Test the robustness of VLMs against backdoor attacks using diffusion models
- Analyze the results of backdoor attacks on VLMs to identify potential vulnerabilities
- Develop defense strategies to mitigate the effects of clean-label backdoor attacks on VLMs
Who Needs to Know This
AI researchers and engineers working on Vision-Language Models can benefit from this knowledge to improve the security and robustness of their models, while data scientists and ML engineers can apply these concepts to other areas of AI security
Key Insight
💡 Diffusion models can be used to generate poisoned samples for backdoor attacks on Vision-Language Models, highlighting the need for robust defense strategies
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🚨 Vision-Language Models are vulnerable to clean-label backdoor attacks via diffusion models! 🚨
Key Takeaways
Learn to defend Vision-Language Models against clean-label backdoor attacks using diffusion models and understand the vulnerability of VLMs to such attacks
Full Article
Title: CBV: Clean-label Backdoor Attacks on Vision Language Models via Diffusion Models
Abstract:
arXiv:2605.02202v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have achieved remarkable success in tasks such as image captioning and visual question answering (VQA). However, as their applications become increasingly widespread, recent studies have revealed that VLMs are vulnerable to backdoor attacks. Existing backdoor attacks on VLMs primarily rely on data poisoning by adding visual triggers and modifying text labels, where the induced image-text mismatch makes poisoned samples
Abstract:
arXiv:2605.02202v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have achieved remarkable success in tasks such as image captioning and visual question answering (VQA). However, as their applications become increasingly widespread, recent studies have revealed that VLMs are vulnerable to backdoor attacks. Existing backdoor attacks on VLMs primarily rely on data poisoning by adding visual triggers and modifying text labels, where the induced image-text mismatch makes poisoned samples
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