Paraphrasing Attack Resilience of Various AI-Generated Text Detection Methods
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Computer Science > Machine Learning
Title:Paraphrasing Attack Resilience of Various AI-Generated Text Detection Methods
Abstract:The recent large-scale emergence of LLMs has left an open space for dealing with their consequences, such as plagiarism or the spread of false information on the Internet. Coupling this with the rise of AI detector bypassing tools, reliable machine-generated text detection is in increasingly high demand. We investigate the paraphrasing attack resilience of various machine-generated text detection methods, evaluating three approaches: fine-tuned RoBERTa, Binoculars, and text feature analysis, along with their ensembles using Random Forest classifiers. We discovered that Binoculars-inclusive ensembles yield the strongest results, but they also suffer the most significant losses during attacks. In this paper, we present the dichotomy of performance versus resilience in the world of AI text detection, which complicates the current perception of reliability among state-of-the-art techniques.
| Comments: | NAACL 2025 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.14240 [cs.LG] |
| (or arXiv:2605.14240v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.14240
arXiv-issued DOI via DataCite (pending registration)
|
|
| Related DOI: | https://doi.org/10.18653/v1/2025.naacl-srw.46
DOI(s) linking to related resources
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
Vision-Based Runtime Monitoring under Varying Specifications using Semantic Latent Representations
May 15
-
Mechanistic Interpretability of EEG Foundation Models via Sparse Autoencoders
May 15
-
Rethinking Molecular OOD Generalization via Target-Aware Source Selection
May 15
-
Unsupervised learning of acquisition variability in structural connectomes via hybrid latent space modeling
May 15
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.