Synthetic Text Detection: Systemic Literature Review
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 59 references that are in the map. First posted on arXiv 1 Oct 2022.
Builds on
22 of 59 references.
References · 22
- 2019Defending Against Neural Fake Newscited by 553
- 2019GLTR: Statistical Detection and Visualization of Generated Textcited by 490
- 2020Evaluation of Text Generation: A Surveycited by 275
- 2019Automatic Detection of Generated Text is Easiest when Humans are Fooledcited by 260
- 2020Automatic Detection of Machine Generated Text: A Critical Surveycited by 160
- 2020TweepFake: about Detecting Deepfake Tweetscited by 117
- 2021Pretrained Language Models for Text Generation: A Surveycited by 95
- 2019The Limitations of Stylometry for Detecting Machine-Generated Fake Newscited by 63
- 2019Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detectioncited by 50
- 2021Artificial Text Detection via Examining the Topology of Attention Mapscited by 49
- 2020Detecting Cross-Modal Inconsistency to Defend Against Neural Fake Newscited by 41
- 2020Neural Deepfake Detection with Factual Structure of Textcited by 40
10 more
- 2020Attacking Neural Text Detectorscited by 37
- 2020RoFT: A Tool for Evaluating Human Detection of Machine-Generated Textcited by 36
- 2022Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russiancited by 29
- 2020MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Modelscited by 25
- 2020CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detectioncited by 23
- 2021Unsupervised and Distributional Detection of Machine-Generated Textcited by 23
- 2022A Benchmark Corpus for the Detection of Automatically Generated Text in Academic Publicationscited by 18
- 2021Fingerprinting Fine-tuned Language Models in the Wildcited by 9
- 2017DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generationcited by 8
- 2022DIALOG-22 RuATD Generated Text Detectioncited by 3
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.