# Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection

arXiv: https://arxiv.org/abs/2406.06558 · HTML: https://mlsanity.com/paper/2406.06558/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 1 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [RADAR: Robust AI-Text Detection via Adversarial Learning](https://mlsanity.com/paper/2307.03838/) (2023)
- [Estimating Continuous Distributions in Bayesian Classifiers](https://mlsanity.com/paper/1302.4964/) (2013)
- [WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection](https://mlsanity.com/paper/2402.11843/) (2024)
- [TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection](https://mlsanity.com/paper/2403.08108/) (2024)
- [Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts](https://mlsanity.com/paper/2403.13786/) (2024)
- [An Empirical Study of AI Generated Text Detection Tools](https://mlsanity.com/paper/2310.01423/) (2023)
- [The effect of source disclosure on evaluation of AI-generated messages: A two-part study](https://mlsanity.com/paper/2311.15544/) (2023)
- [Approximating Human-Like Few-shot Learning with GPT-based Compression](https://mlsanity.com/paper/2308.06942/) (2023)
- [Automated Scoring of Clinical Patient Notes using Advanced NLP and Pseudo Labeling](https://mlsanity.com/paper/2401.12994/) (2024)
- [M2fNet: Multi-modal Forest Monitoring Network on Large-scale Virtual Dataset](https://mlsanity.com/paper/2402.04534/) (2024)

## Built on by (most cited first, 7 of 7)

- [OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrieval](https://mlsanity.com/paper/2507.05631/) (2025)
- [Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments](https://mlsanity.com/paper/2409.14972/) (2024)
- [Benchmarking Large Language Models for Image Classification of Marine Mammals](https://mlsanity.com/paper/2410.19848/) (2024)
- [HULLMI: Human vs LLM identification with explainability](https://mlsanity.com/paper/2409.04808/) (2024)
- [GPT Editors, Not Authors: The Stylistic Footprint of LLMs in Academic Preprints](https://mlsanity.com/paper/2505.17327/) (2025)
- [Two Birds with One Stone: Multi-Task Detection and Attribution of LLM-Generated Text](https://mlsanity.com/paper/2508.14190/) (2025)
- [Detecting AI-Generated Paraphrases in Bengali: A Comparative Study of Zero-Shot and Fine-Tuned Transformers](https://mlsanity.com/paper/2512.21709/) (2025)

## Abstract (arXiv)

The rapid advancement of Large Language Models (LLMs) has ushered in an era where AI-generated text is increasingly indistinguishable from human-generated content. Detecting AI-generated text has become imperative to combat misinformation, ensure content authenticity, and safeguard against malicious uses of AI. In this paper, we propose a novel hybrid approach that combines traditional TF-IDF techniques with advanced machine learning models, including Bayesian classifiers, Stochastic Gradient Descent (SGD), Categorical Gradient Boosting (CatBoost), and 12 instances of Deberta-v3-large models. Our approach aims to address the challenges associated with detecting AI-generated text by leveraging the strengths of both traditional feature extraction methods and state-of-the-art deep learning models. Through extensive experiments on a comprehensive dataset, we demonstrate the effectiveness of our proposed method in accurately distinguishing between human and AI-generated text. Our approach achieves superior performance compared to existing methods. This research contributes to the advancement of AI-generated text detection techniques and lays the foundation for developing robust solutions to mitigate the challenges posed by AI-generated content.