The adversarial game between detection and evasion: A survey of anti-detection techniques for machine-generated texts.

Abstract

With the explosive growth of large language models (LLMs), research on machine-generated text detection (MGTD) has also proliferated. Alongside these developments, a wide range of attack algorithms targeting MGTD systems have emerged. While previous studies have surveyed detection techniques, few have examined the dynamic interplay between attack and defense. Following PRISMA 2020, this paper systematically synthesizes 27 studies of attacks against MGTD and the available evidence on correspondin

Categories

@article{meng2026adversarial,
  title = {{The adversarial game between detection and evasion: A survey of anti-detection techniques for machine-generated texts.}},
  author = {De-Yu Meng and Tad Gonsalves},
  year = {2026},
  month = sep,
  journal = {Neural Networks},
  doi = {10.1016/j.neunet.2026.109562},
  url = {https://www.semanticscholar.org/paper/d918d543553f11905ebae1ae12cb2d221c5fb2e4}
}