arXiv:2505. 14608v3 Announce Type: replace-cross Abstract: Despite considerable progress in the development of machine-text detectors, the ease with which machine-text can be manipulated to evade detection has led to suggestions that the problem is inherently intractable.
arXiv:2606. 09700v1 Announce Type: cross Abstract: Large language model (LLM)-powered content moderation systems have become a critical defense against harmful online content.
arXiv:2606. 14060v1 Announce Type: new Abstract: Adversarial conditions such as paraphrasing and targeted style transfer sharply degrade the accuracy of machine text detectors.
arXiv:2606. 10099v1 Announce Type: cross Abstract: The rapid development of large language models (LLMs) has raised concerns about misuse such as plagiarism, misinformation, and automated influence operations, motivating the need for robust detectors.
arXiv:2604. 25860v2 Announce Type: replace-cross Abstract: Machine-generated text (MGT) detection requires identifying structurally invariant signals across generation models, rather than relying on model-specific fingerprints.
arXiv:2606. 07313v1 Announce Type: cross Abstract: Detecting machine-generated text is especially difficult under distribution shift, such as transfer across domains, source models, and editing attacks.
arXiv:2609.24801v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in production systems, raising concerns about their exposure to adversarial manipulation throu...
arXiv:2605. 00924v2 Announce Type: replace-cross Abstract: AI-generated content (AIGC) detectors are increasingly deployed in high-stakes settings such as academic integrity screening, yet their reliability rests on a fundamental paradox: as language models are trained on human-written corpora, the statistical boundary between AI and human writing will inevitably dissolve as models improve.
arXiv:2606. 04906v1 Announce Type: cross Abstract: Although it is generally agreed that AI-generated text poses a broad societal risk, there is no common understanding in the AI-generated text detection literature on what constitutes harmful use.
arXiv:2606. 31074v1 Announce Type: cross Abstract: Existing AI-generated text detectors are vulnerable to attacks that manipulate textual characteristics.
arXiv:2608. 05430v1 Announce Type: cross Abstract: The remarkable instruction-following ability of modern LLMs has enabled their practical use as the minds of agents that can autonomously complete increasingly complex tasks.
The paper introduces a style-aware paraphrasing method for text anonymization that leverages pretrained large language models to build compact stylistic profiles from minimal samples and rewrite text to suppress identifiable style markers while preserving meaning. It demonstrates that this approach reduces authorship attribution F1 scores by 60‑70% on blog and review datasets, outperforming both differential privacy‑based and non‑DP baselines, and maintains content quality and readability.
The paper introduces TRACE, a lightweight framework for detecting corpus poisoning in Retrieval-Augmented Generation systems. TRACE works by tracing answer-related tokens through token influence attribution, first identifying recurrent high-influence keywords across retrieved documents and then verifying their impact on model predictions. Experiments on three QA benchmarks and six large language models show that TRACE achieves strong detection performance while also revealing attacker-specified target answers.