# Adversarial Style Augmentation via Large Language Model for Robust Fake News Detection

arXiv: https://arxiv.org/abs/2406.11260 · HTML: https://mlsanity.com/paper/2406.11260/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 49 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [RADAR: Robust AI-Text Detection via Adversarial Learning](https://mlsanity.com/paper/2307.03838/) (2023)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017)
- [Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection](https://mlsanity.com/paper/2309.12247/) (2023)
- [Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks](https://mlsanity.com/paper/2310.10830/) (2023)
- [Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation](https://mlsanity.com/paper/2402.05699/) (2024)
- [DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection](https://mlsanity.com/paper/2402.10426/) (2024)
- [Generalizing to the Future: Mitigating Entity Bias in Fake News Detection](https://mlsanity.com/paper/2204.09484/) (2022)
- [Domain Adaptive Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/2202.08159/) (2022)
- [MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models](https://mlsanity.com/paper/2009.01048/) (2020)
- [Attacking Fake News Detectors via Manipulating News Social Engagement](https://mlsanity.com/paper/2302.07363/) (2023)
- [Fake News Detection via NLP is Vulnerable to Adversarial Attacks](https://mlsanity.com/paper/1901.09657/) (2019)
- [Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer](https://mlsanity.com/paper/2209.08902/) (2022)
- [Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.05509/) (2021)
- [Composition-contrastive Learning for Sentence Embeddings](https://mlsanity.com/paper/2307.07380/) (2023)
- [TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection](https://mlsanity.com/paper/2402.07776/) (2024)
- [How Vulnerable Are Automatic Fake News Detection Methods to Adversarial Attacks?](https://mlsanity.com/paper/2107.07970/) (2021)
- [Constraint 2021: Machine Learning Models for COVID-19 Fake News Detection Shared Task](https://mlsanity.com/paper/2101.03717/) (2021)

## Built on by (most cited first, 6 of 6)

- [Robust Fake News Detection using Large Language Models under Adversarial Sentiment Attacks](https://mlsanity.com/paper/2601.15277/) (2026)
- [Knowing without Acting: The Disentangled Geometry of Safety Mechanisms in Large Language Models](https://mlsanity.com/paper/2603.05773/) (2026)
- [Agentic Multi-Persona Framework for Evidence-Aware Fake News Detection](https://mlsanity.com/paper/2512.21039/) (2025)
- [Depth Charge: Jailbreak Large Language Models from Deep Safety Attention Heads](https://mlsanity.com/paper/2603.05772/) (2026)
- [Ahead of the Spread: Agent-Driven Virtual Propagation for Early Fake News Detection](https://mlsanity.com/paper/2601.02750/) (2026)
- [Leveraging LLM-Generated Explanations for Detecting Emotionally Rewritten Fake News](https://mlsanity.com/paper/2610.08835/) (2026)

## Abstract (arXiv)

The spread of fake news harms individuals and presents a critical social challenge that must be addressed. Although numerous algorithmic and insightful features have been developed to detect fake news, many of these features can be manipulated with style-conversion attacks, especially with the emergence of advanced language models, making it more difficult to differentiate from genuine news. This study proposes adversarial style augmentation, AdStyle, designed to train a fake news detector that remains robust against various style-conversion attacks. The primary mechanism involves the strategic use of LLMs to automatically generate a diverse and coherent array of style-conversion attack prompts, enhancing the generation of particularly challenging prompts for the detector. Experiments indicate that our augmentation strategy significantly improves robustness and detection performance when evaluated on fake news benchmark datasets.