DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis

ArXi:2604.26328v1 Announce Type: cross The rapid advancement of large language models (LLMs) presents new security challenges, particularly in detecting machine-generated text used for misinformation, impersonation, and content forgery. Most existing detection approaches struggle with robustness against adversarial perturbation, paraphrasing attacks, and domain shifts, often requiring restrictive access to model parameters or large labeled datasets. To address this, we propose DSIPA, a novel.