Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.18193 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910029841432576 |
|---|---|
| author | Yang, Yiran Liu, Zhaowei Yuan, Yuan Song, Yukun Ma, Xiong Song, Yinghao Zeng, Xiangji Sun, Lu Wang, Yulu Zhou, Hai Cui, Shuai Gong, Zhaohan Zhang, Jiefei |
| author_facet | Yang, Yiran Liu, Zhaowei Yuan, Yuan Song, Yukun Ma, Xiong Song, Yinghao Zeng, Xiangji Sun, Lu Wang, Yulu Zhou, Hai Cui, Shuai Gong, Zhaohan Zhang, Jiefei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18193 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BLM-Guard: Explainable Multimodal Ad Moderation with Chain-of-Thought and Policy-Aligned Rewards Yang, Yiran Liu, Zhaowei Yuan, Yuan Song, Yukun Ma, Xiong Song, Yinghao Zeng, Xiangji Sun, Lu Wang, Yulu Zhou, Hai Cui, Shuai Gong, Zhaohan Zhang, Jiefei Computer Vision and Pattern Recognition 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. |
| title | BLM-Guard: Explainable Multimodal Ad Moderation with Chain-of-Thought and Policy-Aligned Rewards |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.18193 |