Does detectgpt fully utilize perturbation? bridging selective perturbation to fine-tuned contrastive learning detector would be better

S Liu, X Liu, Y Wang, Z Cheng, C Li… - Proceedings of the …, 2024 - aclanthology.org
The burgeoning generative capabilities of large language models (LLMs) have raised
growing concerns about abuse, demanding automatic machine-generated text detectors …

FDLLM: A Text Fingerprint Detection Method for LLMs in Multi-Language, Multi-Domain Black-Box Environments

Z Fu, J Chen, H Sun, T Yang, R Li, Y Zhang - arxiv preprint arxiv …, 2025 - arxiv.org
Using large language models (LLMs) integration platforms without transparency about
which LLM is being invoked can lead to potential security risks. Specifically, attackers may …

MAGRET: Machine-generated Text Detection with Rewritten Texts

Y Huang, J Cao, H Luo, X Guan… - Proceedings of the 31st …, 2025 - aclanthology.org
With the quick advancement in text generation ability of Large Language Mode (LLM),
concerns about the misuse of machine-generated content have grown, raising potential …

DART: An AIGT Detector using AMR of Rephrased Text

H Park, B Kim, B Kim - arxiv preprint arxiv:2412.11517, 2024 - arxiv.org
As large language models (LLMs) generate more human-like texts, concerns about the side
effects of AI-generated texts (AIGT) have grown. So, researchers have developed methods …

Impact of Spelling and Editing Correctness on Detection of LLM-Generated Emails

P Gryka, K Gradoń, M Kozłowski… - … 19th Conference on …, 2024 - ieeexplore.ieee.org
In this paper, we investigated the impact of spelling and editing correctness on the accuracy
of detection if an email was written by a human or if it was generated by a language model …