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

• URL: http://arxiv.org/abs/2604.26328v1
• Date: Wed, 29 Apr 2026 06:22:08 GMT
• Title: DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis
• Abstract summary: DSIPA is a training-free framework that detects large language models (LLMs) content by quantifying sentiment distributional stability under controlled stylistic variation.<n>It operates in a zero-shot, black-box manner, leveraging two unsupervised metrics, sentiment distribution consistency and sentiment distribution preservation.<n>Our framework exhibits superior generalizability across domains and strong resilience to adversarial conditions.
• Score: 13.9689104714669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 training-free framework that detects LLM-generated content by quantifying sentiment distributional stability under controlled stylistic variation. It is based on the observation that LLMs typically exhibit more emotionally consistent outputs, while human-written texts display greater affective variation. Our framework operates in a zero-shot, black-box manner, leveraging two unsupervised metrics, sentiment distribution consistency and sentiment distribution preservation, to capture these intrinsic behavioral asymmetries without the need for parameter updates or probability access. Extensive experiments are conducted on state-of-the-art proprietary and open-source models, including GPT-5.2, Gemini-1.5-pro, Claude-3, and LLaMa-3.3. Evaluations on five domains, such as news articles, programming code, student essays, academic papers, and community comments, demonstrate that DSIPA improves F1 detection scores by up to 49.89% over baseline methods. The framework exhibits superior generalizability across domains and strong resilience to adversarial conditions, providing a robust and interpretable behavioral signal for secure content identification in the evolving LLM landscape.

Related papers

• MS-MFAD : Multimodal large language models for Face Anti-spoofing Detection [25.077055201475442]
  We propose Multimodal Large Language Models (MFAD) for face anti-spoofing detection.<n>MFAD activates intrinsic reasoning capabilities of Multimodal Large Language Models via a fine-grained pixel-semantic anchoring mechanism.<n>By annotating only 1,000 precise masks per attack category, we generate reasoning evidence chains strictly corresponding to spoofed regions.
  arXiv  Detail & Related papers  (2026-08-18T03:38:59Z)
• Lightweight Stylistic Consistency Profiling: Robust Detection of LLM-Generated Textual Content for Multimedia Moderation [17.622520491018438]
  Large Language Models (LLMs) in content creation has made distinguishing human-written textual content a critical task for multimedia moderation.<n>Existing detectors often rely on statistical cues or model-specifics, making them vulnerable to paraphrasing and adversarial manipulations.<n>We propose LiLi SCP, a novel lightweight stylistic consistency profiling method for robust detection of LLM-generated textual content.
  arXiv  Detail & Related papers  (2026-05-07T09:58:00Z)
• DeLeaker: Dynamic Inference-Time Reweighting For Semantic Leakage Mitigation in Text-to-Image Models [55.30555646945055]
  Text-to-Image (T2I) models are vulnerable to semantic leakage.<n>We introduce DeLeaker, a lightweight approach that mitigates leakage by directly intervening on the model's attention maps.<n>SLIM is the first dataset dedicated to semantic leakage.
  arXiv  Detail & Related papers  (2025-10-16T17:39:21Z)
• RHINO: Guided Reasoning for Mapping Network Logs to Adversarial Tactics and Techniques with Large Language Models [9.065322387043546]
  We introduce RHINO, a framework that decomposes Large Language Models into three interpretable phases mirroring human reasoning.<n> RHINO bridges the semantic gap between low-level observations and adversarial intent while improving output reliability through structured reasoning.<n>Our results demonstrate that RHINO significantly enhances the interpretability and scalability of threat analysis, offering a blueprint for deploying LLMs in operational security settings.
  arXiv  Detail & Related papers  (2025-10-16T02:25:46Z)
• StyleDecipher: Robust and Explainable Detection of LLM-Generated Texts with Stylistic Analysis [18.44456241158174]
  StyleDecipher is a robust and explainable detection framework.<n>It revisits text detection using combined feature extractors to quantify stylistic differences.<n>It consistently achieves state-of-the-art in-domain accuracy.
  arXiv  Detail & Related papers  (2025-10-14T15:07:27Z)
• Diversity Boosts AI-Generated Text Detection [51.56484100374058]
  DivEye is a novel framework that captures how unpredictability fluctuates across a text using surprisal-based features.<n>Our method outperforms existing zero-shot detectors by up to 33.2% and achieves competitive performance with fine-tuned baselines.
  arXiv  Detail & Related papers  (2025-09-23T10:21:22Z)
• RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns [50.401907401444404]
  Large language models (LLMs) are crucial for preventing misuse and building trustworthy AI systems.<n>We propose RepreGuard, an efficient statistics-based detection method.<n> Experimental results show that RepreGuard outperforms all baselines with average 94.92% AUROC on both in-distribution (ID) and OOD scenarios.
  arXiv  Detail & Related papers  (2025-08-18T17:59:15Z)
• When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness Methods for LLMs [55.20230501807337]
  We present the first systematic evaluation of 5 methods for improving prompt robustness within a unified experimental framework.<n>We benchmark these techniques on 8 models from Llama, Qwen and Gemma families across 52 tasks from Natural Instructions dataset.
  arXiv  Detail & Related papers  (2025-08-15T10:32:50Z)
• Model-Agnostic Sentiment Distribution Stability Analysis for Robust LLM-Generated Texts Detection [23.153044933861988]
  We propose SentiDetect, a model-agnostic framework for detecting large language models (LLMs)-generated text.<n>Our method is motivated by the empirical observation that LLM outputs tend to exhibit emotionally consistent patterns, whereas human-written texts display greater emotional variability.<n>We evaluate SentiDetect on five diverse datasets and a range of advanced LLMs, including Gemini-1.5-Pro, Claude-3, GPT-4-0613, and LLaMa-3.3.
  arXiv  Detail & Related papers  (2025-08-09T09:55:47Z)
• Training-free LLM-generated Text Detection by Mining Token Probability Sequences [18.955509967889782]
  Large language models (LLMs) have demonstrated remarkable capabilities in generating high-quality texts across diverse domains. Training-free methods, which focus on inherent discrepancies through carefully designed statistical features, offer improved generalization and interpretability. We introduce a novel training-free detector, termed textbfLastde that synergizes local and global statistics for enhanced detection.
  arXiv  Detail & Related papers  (2024-10-08T14:23:45Z)
• Detecting Machine-Generated Long-Form Content with Latent-Space Variables [54.07946647012579]
  Existing zero-shot detectors primarily focus on token-level distributions, which are vulnerable to real-world domain shifts. We propose a more robust method that incorporates abstract elements, such as event transitions, as key deciding factors to detect machine versus human texts.
  arXiv  Detail & Related papers  (2024-10-04T18:42:09Z)
• In and Out-of-Domain Text Adversarial Robustness via Label Smoothing [64.66809713499576]
  We study the adversarial robustness provided by various label smoothing strategies in foundational models for diverse NLP tasks. Our experiments show that label smoothing significantly improves adversarial robustness in pre-trained models like BERT, against various popular attacks. We also analyze the relationship between prediction confidence and robustness, showing that label smoothing reduces over-confident errors on adversarial examples.
  arXiv  Detail & Related papers  (2022-12-20T14:06:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.