Unifying Adversarially Robust Model Experts in Vision-Language Models
Vision-language models (VLMs), such as CLIP, are vulnerable to adversarial attacks, posing a serious problem for real-life applications and deployment. Adversarial fine-tuning emerges as a prominent defense method; however, different fine-tuning strategies often produce specialized models with distinct robustness characteristics. Each fine-tuned model in turn thrives in some evaluation settings but falters on others, limiting their defensive capabilities. We refer to these specialized fine-tuned models as robust model experts and propose a collaborative adversarial fine-tuning framework: CARE - Collaborative Adversarial Robustness fine-tuning using Embedding alignment. CARE maintains mult...
| Authors | Nguyen Duc Thai, Junhao Dong, Sua Qi Rong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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