A patch-augmented cross-view regularization method reduces backdoor attack success rates in multimodal LLMs by enforcing output differences between original and perturbed views while using entropy constraints to preserve benign generation quality.
In: Machine Learning for Health (ML4H), pp
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A Patch-based Cross-view Regularized Framework for Backdoor Defense in Multimodal Large Language Models
A patch-augmented cross-view regularization method reduces backdoor attack success rates in multimodal LLMs by enforcing output differences between original and perturbed views while using entropy constraints to preserve benign generation quality.