SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations

With the rise of generative AI and rapid growth of high-quality video generation, video guardrails have become more crucial than ever to ensure safety and security across platforms. Current video guardrails, however, are either overly simplistic, relying on pure classification models trained on simple policies with limited unsafe categories, which lack detailed explanations, or prompting multimodal large language models (MLLMs) with long safety guidelines, which are inefficient and impractical for guardrailing real-world content. To bridge this gap, we propose SAFEWATCH, an efficient MLLM-based video guardrail model designed to follow customized safety policies and provide multi-label video guardrail outputs with content-specific explanations in a zero-shot manner. In particular, unlike traditional MLLM-based guardrails that encode all safety policies autoregressively, causing inefficiency and bias, SAFEWATCH uniquely encodes each policy chunk in parallel and eliminates their position bias such that all policies are attended simultaneously with equal importance. In addition, to improve efficiency and accuracy, SAFEWATCH incorporates a policy-aware visual token pruning algorithm that adaptively selects the most relevant video tokens for each policy, discarding noisy or irrelevant information. This allows for more focused, policy-compliant guardrail with significantly reduced computational overhead. Considering the limitations of existing video guardrail benchmarks, we propose SAFEWATCH-BENCH, a large-scale video guardrail benchmark comprising over 2M videos spanning six safety categories which covers over 30 tasks to ensure a comprehensive coverage of all potential safety scenarios. We have conducted extensive experiments, showing that SAFEWATCH outperforms all SOTA video guardrails on SAFEWATCH-BENCH by 28.2%, and achieves a 13.6% improvement on existing benchmarks, all while reducing inference costs by an average of 10%. SAFE-WATCH also demonstrates strong policy-following abilities and outperforms previous SOTAs by 5.6% and 15.6% in zero-shot generalizability to new policies and new prompting tasks. Additionally, both LLM-as-a-judge and human evaluators confirm the high quality of the explanations provided by SAFEWATCH. Our project is open-sourced at https://safewatch-aiguard.github.io .