A Patch-based Cross-view Regularized Framework for Backdoor Defense in Multimodal Large Language Models
Multimodal large language models have become an important infrastructure for unified processing of visual and linguistic tasks. However, such models are highly susceptible to backdoor implantation during supervised fine-tuning and will steadily output the attacker's predefined harmful responses once a specific trigger pattern is activated. The core challenge of backdoor defense lies in suppressing attack success under low poisoning ratios while preserving the model's normal generation ability. These two objectives are inherently conflicting. Strong suppression often degrades benign performance, whereas weak regularization fails to mitigate backdoor behaviors. To this end, we propose a uni...
| Authors | Tianmeng Fang, Yong Wang, Zetai Kong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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