New attack framework fools AI models using single CLIP model

Researchers have developed a new adversarial attack framework called UnivIntruder that can fool deep neural networks using a single, publicly available CLIP model. This method generates universal, transferable, and targeted adversarial perturbations based on textual concepts, achieving high success rates on datasets like ImageNet and CIFAR-10. Notably, UnivIntruder demonstrates real-world vulnerabilities by compromising image search engines such as Google and Baidu, as well as vision-language models like GPT-4 and Claude 3.5, even without direct model querying. AI

IMPACT Highlights significant security vulnerabilities in current AI models and search engines, necessitating new defense strategies.

RANK_REASON The cluster is a research paper detailing a new adversarial attack method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Baidu
• Binyan Xu
• CIFAR-10
• Claude 3.5 Sonnet
• GPT-4
• ImageNet
• UnivIntruder

• paper
• safety

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