Kill two birds with one stone: generalized and robust AI-generated text detection via dynamic perturbations
This addresses the need for reliable detection of AI-generated text to prevent misuse, offering a unified solution that is incremental by combining generalization and robustness aspects.
The paper tackles the problem of detecting AI-generated text by proposing a method that simultaneously improves generalization across domains and robustness against adversarial attacks, achieving state-of-the-art performance in cross-domain scenarios and under text adversarial attacks.
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. However, 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 argue that robustness can be view as a specific form of domain shift, and empirically reveal an intrinsic mechanism for model generalization 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. The code is publicly available at https://github.com/CAU-ISS-Lab/AIGT-Detection-Evade-Detection/tree/main/DP-Net.