Short-video platforms now host vast multimodal ads whose deceptive visuals, speech and subtitles demand finer-grained, policy-driven moderation than community safety filters. We present BLM-Guard, a content-audit framework for commercial ads that fuses Chain-of-Thought reasoning with rule-based policy principles and a critic-guided reward. A rule-driven ICoT data-synthesis pipeline jump-starts training by generating structured scene descriptions, reasoning chains and labels, cutting annotation costs. Reinforcement learning then refines the model using a composite reward balancing causal coherence with policy adherence. A multitask architecture models intra-modal manipulations (e.g., exaggerated imagery) and cross-modal mismatches (e.g., subtitle-speech drift), boosting robustness. Experiments on real short-video ads show BLM-Guard surpasses strong baselines in accuracy, consistency and generalization.
Figures & tables
Figure 1: BLM-Guard Benchmark Taxonomy. Our benchmark organizes commercial short-video ads into a hierarchical risk taxonomy with seven core violation scenarios and fine-grained subtypes. Each node reflects a policy-sensitive violation type (e.g., income exaggeration, privacy leak, feudal superstition), and is further associated with a severity level (high, medium, low). This structure supports interpretable supervision and enables fine-grained evaluation of model performance on diverse and nuanced moderation cases. A dedicated “No Risk” category is also included to balance risk distribution.
Figure 2: Our method adopts a progressive two-stage pipeline for policy-controllable content moderation. In Stage 1 (Rule-driven SFT Cold Start), we synthesize structured visual-language Chain-of-Thought (ICoT) data via keyframe selection and multi-step prompting using InternVL. This enables supervised fine-tuning with rule-anchored causal supervision. In Stage 2 (Self-adaptive GRPO Reinforcement Learning), we apply a safety-aware data curation strategy and propose a Self-Adaptive Critique Reward (SACR) to dynamically evaluate reasoning outputs. The model is optimized using a modified Group-wise Relative Policy Optimization (GRPO) algorithm with token-level normalization and dynamic sampling. This multi-stage process enables the model to first learn fine-grained compliance reasoning and then refine its moderation behavior through adaptive, reward-driven training.
Algorithm 1 BLM-Guard: Self-Adaptive GRPO Training
Figure 3: Comprehensive comparison of model strategies and components. Left : Radar plots highlight improvements in accuracy, precision, and consistency across key metrics. Middle : Heatmaps visualize performance across fine-grained risk scenarios and external benchmarks. Right : Histograms show the distributional gains in consistency induced by Rule-SFT and SCA-R reward learning.
Video content moderation detects and explains harmful, misleading, or policy-violating material in videos, including its visual, audio, and temporal context.