@inproceedings{zhang-etal-2025-aiguard,
    title = "{AIG}uard: A Benchmark and Lightweight Detection for {E}-commerce {AIGC} Risks",
    author = "Zhang, Wenhua  and
      Li, Weicheng  and
      Rao, Xuanrong  and
      Zou, Lixin  and
      Luo, Xiangyang  and
      Zhuang, Chubin  and
      Hong, Yongjie  and
      Qin, Zhen  and
      Chang, Hengyu  and
      Li, Chenliang  and
      Zheng, Bo",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.643/",
    pages = "12437--12450",
    ISBN = "979-8-89176-256-5",
    abstract = "Recent advancements in AI-generated content (AIGC) have heightened concerns about harmful outputs, such as misinformation and malicious misuse.Existing detection methods face two key limitations:(1) lacking real-world AIGC scenarios and corresponding risk datasets, and(2) both traditional and multimodal large language models (MLLMs) struggle to detect risks in AIGC.Towards this end, we introduce **AIGuard**, the first benchmark for AIGC risk detection in real-world e-commerce. It includes 253,420 image-text pairs (i.e., the risk content and risk description) across four critical categories: *abnormal body*, *violating physical laws*, *misleading or illogical context*, and *harmful or problematic message*.To effectively detect these risks, we propose distilling text annotations into dense soft prompts and identifying risk content through image soft prompt matching during inference.Experiments on the benchmark show that this method achieves a 9.68{\%} higher recall than leading multimodal models while using only 25{\%} of the training resources and improving inference speed by 37.8 times.For further research, our benchmark and code are available at [https://github.com/wenh-zhang/aiguard-dataset](https://github.com/wenh-zhang/aiguard-dataset)."
}