  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
\url{https://github.com/CarlanLark/Robust-AIGC-Detector}.
