Towards Fine-grained and Robust Detection of Human-AI Co-authorship

The paper introduces a discrete-class framework for detecting human‑AI co‑authorship, distinguishing between superficial AI polishing and deeper AI‑driven discourse changes. It shows that adversarial paraphrasing can obscure detection signals, but augmenting training data with such paraphrases improves detector robustness across multiple datasets and generation models. The study demonstrates that principled data augmentation and a fine‑grained classification approach are essential for reliable co‑authorship detection.

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• llms
• benchmarks
• safety