[AAAI 2025 oral] Official repository of Imitate Before Detect: Aligning Machine Stylistic Preference for Machine-Revised Text Detection

This project aims to address this gap by conducting a systematic, controlled study of human versus LLM-generated text detectability using paired question–answer datasets. Rather than proposing a novel detection architecture, the focus is on analyzing detection robustness, failure modes, and the impact of adversarial humanization strategies.

Detect and eliminate AI writing patterns in your content. This Claude Code plugin performs multi-tier analysis of character patterns, language cues, structural issues, and voice authenticity. Auto-fix em dashes, smart quotes, and emojis. Keep documentation and prose sounding genuinely human.

Best static AI text humanizer. Two research-grounded skills that work in any LLM (Claude, ChatGPT, Gemini, Codex): humanize beats perplexity-based detectors, ai-check produces forensic scoring with evidence-quoted flags. Nine levers, 50+ peer-reviewed sources, 2024-2026 detection literature.

Professional text refinement, AI detection, and style conversion services. 专业文本润色、AI检测和风格转换服务

Projects concerning LLMs, prompting, NLP, webscraping, data aquisition and dataset analysis.

6-class text authorship detection pipeline for human and LLM-generated text using TF-IDF, stylometric features, and stacked scikit-learn/LightGBM models for the MALTO Hackathon 2026 (F1: 0.9567).

🎲 Detect whether a GitHub repo's code was likely written by an LLM. Zero dependencies. Scores repos 0-100 using commit velocity, session analysis, burst detection, message patterns, and project-scale plausibility.

Catch AI code mistakes before they ship — 50+ checks for hallucinated APIs, stub functions, hardcoded secrets, and SQL injection. SARIF output for GitHub Code Scanning.

Python tool for simple comparison check on generated code vs suspected generated code.

Proof of concept tool to bypass document replay technology (such as GPTZero).

The official repository for our ACL 2025 paper, "Who Writes What: Unveiling the Impact of Author Roles on AI-generated Text Detection"

A unified tool for testing and using LLM detectors

Detects AI-generated essays using an ensemble of LightGBM, CatBoost, Naive Bayes, SGD, and Random Forest. Custom BPE tokenizer built with Hugging Face + TF-IDF vectorization with 3-5 word n-grams. Weighted soft-voting classifier.

Detecting AI coding assistance in famous OSS contributors — style drift analysis across 10 developers

O Hybrid LLM Text Detection é um projeto de estudo e experimentação em Machine Learning e Processamento de Linguagem Natural (NLP) voltado para a identificação de textos gerados por modelos de linguagem. O projeto foi inspirado na competição do Kaggle LLM - Detect AI Generated Text.

Engine for detecting LLM-generated credentials. (Beta)

Extension Chrome MV3 de détection de contenus générés par IA (texte, images, vidéos, audio). Heuristiques locales, APIs externes optionnelles, signalement communautaire et inspection HTML/sécurité.

MCP server for the Chief Editor AI slop detector — analyses text for AI-generated writing patterns