@inproceedings{zhou-etal-2025-kill,
    title = "Kill two birds with one stone: generalized and robust {AI}-generated text detection via dynamic perturbations",
    author = "Zhou, Yinghan  and
      Wen, Juan  and
      Peng, Wanli  and
      Yiming, Xue  and
      Zhang, ZiWei  and
      Zhengxian, Wu",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.446/",
    pages = "8864--8875",
    ISBN = "979-8-89176-189-6",
    abstract = "The growing popularity of large language models has raised concerns regarding the potential to misuse AI-generated text (AIGT). It becomes increasingly critical to establish an excellent AIGT detection method with high generalization and robustness.While, existing methods either focus on model generalization or concentrate on robustness.The unified mechanism, to simultaneously address the challenges of generalization and robustness, is less explored. In this paper, we first empirically reveal an intrinsic mechanism for model generalization and robustness of AIGT detection task.Then, we proposed a novel AIGT detection method (DP-Net) via dynamic perturbations introduced by a reinforcement learning with elaborated reward and action.Experimentally, extensive results show that the proposed DP-Net significantly outperforms some state-of-the-art AIGT detection methods for generalization capacity in three cross-domain scenarios.Meanwhile, the DP-Net achieves best robustness under two text adversarial attacks."
}