# Synthetic Text Detection: Systemic Literature Review

arXiv: https://arxiv.org/abs/2210.06336 · HTML: https://mlsanity.com/paper/2210.06336/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 59 references that are in the map.
- First posted on arXiv 1 Oct 2022.

## Builds on (most cited first, 22 of 22)

- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [Evaluation of Text Generation: A Survey](https://mlsanity.com/paper/2006.14799/) (2020)
- [Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://mlsanity.com/paper/1911.00650/) (2019)
- [Automatic Detection of Machine Generated Text: A Critical Survey](https://mlsanity.com/paper/2011.01314/) (2020)
- [TweepFake: about Detecting Deepfake Tweets](https://mlsanity.com/paper/2008.00036/) (2020)
- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2105.10311/) (2021)
- [The Limitations of Stylometry for Detecting Machine-Generated Fake News](https://mlsanity.com/paper/1908.09805/) (2019)
- [Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection](https://mlsanity.com/paper/1907.09177/) (2019)
- [Artificial Text Detection via Examining the Topology of Attention Maps](https://mlsanity.com/paper/2109.04825/) (2021)
- [Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News](https://mlsanity.com/paper/2009.07698/) (2020)
- [Neural Deepfake Detection with Factual Structure of Text](https://mlsanity.com/paper/2010.07475/) (2020)
- [Attacking Neural Text Detectors](https://mlsanity.com/paper/2002.11768/) (2020)
- [RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text](https://mlsanity.com/paper/2010.03070/) (2020)
- [Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian](https://mlsanity.com/paper/2206.01583/) (2022)
- [MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models](https://mlsanity.com/paper/2009.01048/) (2020)
- [CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection](https://mlsanity.com/paper/2004.01881/) (2020)
- [Unsupervised and Distributional Detection of Machine-Generated Text](https://mlsanity.com/paper/2111.02878/) (2021)
- [A Benchmark Corpus for the Detection of Automatically Generated Text in Academic Publications](https://mlsanity.com/paper/2202.02013/) (2022)
- [Fingerprinting Fine-tuned Language Models in the Wild](https://mlsanity.com/paper/2106.01703/) (2021)
- [DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generation](https://mlsanity.com/paper/1712.05419/) (2017)
- [DIALOG-22 RuATD Generated Text Detection](https://mlsanity.com/paper/2206.08029/) (2022)

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

Within the text analysis and processing fields, generated text attacks have been made easier to create than ever before. To combat these attacks open sourcing models and datasets have become a major trend to create automated detection algorithms in defense of authenticity. For this purpose, synthetic text detection has become an increasingly viable topic of research. This review is written for the purpose of creating a snapshot of the state of current literature and easing the barrier to entry for future authors. Towards that goal, we identified few research trends and challenges in this field.