The widespread use of large language models (LLMs) has sparked concerns about
the potential misuse of AI-generated text, as these models can produce content
that closely resembles human-generated text. Current detectors for AI-generated
text (AIGT) lack robustness against adversarial perturbations, with even minor
changes in characters or words causing a reversal in distinguishing between
human-created and AI-generated text. This paper investigates the robustness of
existing AIGT detection methods and introduces a novel detector, the Siamese
Calibrated Reconstruction Network (SCRN). The SCRN employs a reconstruction
network to add and remove noise from text, extracting a semantic representation
that is robust to local perturbations. We also propose a siamese calibration
technique to train the model to make equally confidence predictions under
different noise, which improves the model's robustness against adversarial
perturbations. Experiments on four publicly available datasets show that the
SCRN outperforms all baseline methods, achieving 6.5\%-18.25\% absolute
accuracy improvement over the best baseline method under adversarial attacks.
Moreover, it exhibits superior generalizability in cross-domain, cross-genre,
and mixed-source scenarios. The code is available at
https://github.com/CarlanLark/Robust-AIGC-Detector.

本文研究了现有的 AI 生成文本检测方法的鲁棒性，并介绍了一种新的检测器 ——Siamese Calibrated Reconstruction Network（SCRN）。SCRN 采用重构网络来添加和去除文本中的噪声，从而提取出对局部扰动具有鲁棒性的语义表示。我们还提出了一种孪生校准技术来训练模型，在不同的噪声下做出相同的置信度预测，从而提高模型对抗性扰动的鲁棒性。在四个公开数据集上的实验证明，SCRN 在对抗攻击下相对于最佳基线方法取得了 6.5％-18.25％的绝对准确率改进。此外，在跨领域、跨种类和混合来源场景下展现了卓越的泛化能力。代码可在 https://github.com/CarlanLark/Robust-AIGC-Detector 获取。