# Neural Deepfake Detection with Factual Structure of Text

arXiv: https://arxiv.org/abs/2010.07475 · HTML: https://mlsanity.com/paper/2010.07475/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 30 of 41)

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- [Stylometric Detection of AI-Generated Text in Twitter Timelines](https://mlsanity.com/paper/2303.03697/) (2023)
- [M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection](https://mlsanity.com/paper/2402.11175/) (2024)
- [Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective](https://mlsanity.com/paper/2210.10488/) (2022)
- [GPT-who: An Information Density-based Machine-Generated Text Detector](https://mlsanity.com/paper/2310.06202/) (2023)
- [Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites](https://mlsanity.com/paper/2305.09820/) (2023)
- [Deepfake Text Detection: Limitations and Opportunities](https://mlsanity.com/paper/2210.09421/) (2022)
- [Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models](https://mlsanity.com/paper/2402.18059/) (2024)
- [CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learning](https://mlsanity.com/paper/2212.10341/) (2022)
- [SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection](https://mlsanity.com/paper/2404.14183/) (2024)
- [Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods](https://mlsanity.com/paper/2203.05227/) (2022)
- [Detecting AI-Generated Text: Factors Influencing Detectability with Current Methods](https://mlsanity.com/paper/2406.15583/) (2024)
- [Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?](https://mlsanity.com/paper/2304.01002/) (2023)
- [A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterization](https://mlsanity.com/paper/2403.01152/) (2024)
- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [Paraphrase Identification with Deep Learning: A Review of Datasets and Methods](https://mlsanity.com/paper/2212.06933/) (2022)
- [Zero-Shot Detection of LLM-Generated Text using Token Cohesiveness](https://mlsanity.com/paper/2409.16914/) (2024)
- [IMGTB: A Framework for Machine-Generated Text Detection Benchmarking](https://mlsanity.com/paper/2311.12574/) (2023)
- [Stochastic Parrots Looking for Stochastic Parrots: LLMs are Easy to Fine-Tune and Hard to Detect with other LLMs](https://mlsanity.com/paper/2304.08968/) (2023)
- [Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/2405.00711/) (2024)
- [Automatic Detection of Entity-Manipulated Text using Factual Knowledge](https://mlsanity.com/paper/2203.10343/) (2022)
- [Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks](https://mlsanity.com/paper/2310.16955/) (2023)
- [Large Language Model Sourcing: A Survey](https://mlsanity.com/paper/2510.10161/) (2025)
- [Detection of Fake Generated Scientific Abstracts](https://mlsanity.com/paper/2304.06148/) (2023)

## Abstract (arXiv)

Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing approaches to deepfake detection typically represent documents with coarse-grained representations. However, they struggle to capture factual structures of documents, which is a discriminative factor between machine-generated and human-written text according to our statistical analysis. To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text. Our approach represents the factual structure of a given document as an entity graph, which is further utilized to learn sentence representations with a graph neural network. Sentence representations are then composed to a document representation for making predictions, where consistent relations between neighboring sentences are sequentially modeled. Results of experiments on two public deepfake datasets show that our approach significantly improves strong base models built with RoBERTa. Model analysis further indicates that our model can distinguish the difference in the factual structure between machine-generated text and human-written text.