ProGuard: Towards Proactive Multimodal Safeguard
The rapid evolution of generative models has led to a continuous emergence of multimodal safety risks, exposing the limitations of existing defense methods. To address these challenges, we propose ProGuard, a vision-language proactive guard that identifies and describes out-of-distribution (OOD) safety risks without the need for model adjustments required by traditional reactive approaches. We first construct a modality-balanced dataset of 87K samples, each annotated with both binary safety labels and risk categories under a hierarchical multimodal safety taxonomy, effectively mitigating modality bias and ensuring consistent moderation across text, image, and text-image inputs. Based on this dataset, we train our vision-language base model purely through reinforcement learning (RL) to achieve efficient and concise reasoning. To approximate proactive safety scenarios in a controlled setting, we further introduce an OOD safety category inference task and augment the RL objective with a synonym-bank-based similarity reward that encourages the model to generate concise descriptions for unseen unsafe categories. Experimental results show that ProGuard achieves performance comparable to closed-source large models on binary safety classification, substantially outperforms existing open-source guard models on unsafe content categorization. Most notably, ProGuard delivers a strong proactive moderation ability, improving OOD risk detection by 52.6% and OOD risk description by 64.8%.
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Shaohan Yu
claimed_verified
2025-12-31T20:56:28.770Z
68f59ae49315a06ad9a01464
https://cdn-avatars.huggingface.co/v1/production/uploads/68f59ae49315a06ad9a01464/O_l5kZTYlNeIt0h0f-8_Y.png
Sean Yu
yushaohan
user
yushaohan
69535cbc89916ff627aa4041
Lijun Li
claimed_verified
2025-12-31T20:56:25.748Z
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Lijun Li
adwardlee
user
adwardlee
69535cbc89916ff627aa4042
Chenyang Si
69535cbc89916ff627aa4043
Lu Sheng
69535cbc89916ff627aa4044
Jing Shao
ProGuard: Towards Proactive Multimodal Safeguard
The rapid evolution of generative models has led to a continuous emergence of multimodal safety risks, exposing the limitations of existing defense methods. To address these challenges, we propose ProGuard, a vision-language proactive guard that identifies and describes out-of-distribution (OOD) safety risks without the need for model adjustments required by traditional reactive approaches. We first construct a modality-balanced dataset of 87K samples, each annotated with both binary safety labels and risk categories under a hierarchical multimodal safety taxonomy, effectively mitigating modality bias and ensuring consistent moderation across text, image, and text-image inputs. Based on this dataset, we train our vision-language base model purely through reinforcement learning (RL) to achieve efficient and concise reasoning. To approximate proactive safety scenarios in a controlled setting, we further introduce an OOD safety category inference task and augment the RL objective with a synonym-bank-based similarity reward that encourages the model to generate concise descriptions for unseen unsafe categories. Experimental results show that ProGuard achieves performance comparable to closed-source large models on binary safety classification, substantially outperforms existing open-source guard models on unsafe content categorization. Most notably, ProGuard delivers a strong proactive moderation ability, improving OOD risk detection by 52.6% and OOD risk description by 64.8%.
ProGuard is a vision-language model that proactively identifies and describes out-of-distribution safety risks in multimodal content through reinforcement learning and a balanced dataset with hierarchical safety annotations.
vision-language model out-of-distribution reinforcement learning multimodal safety hierarchical taxonomy proactive guard safety classification risk detection risk description