# Adversarial machine learning for protecting against online manipulation

arXiv: https://arxiv.org/abs/2111.12034 · HTML: https://mlsanity.com/paper/2111.12034/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 23 Nov 2021.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017)
- [TextBugger: Generating Adversarial Text Against Real-world Applications](https://mlsanity.com/paper/1812.05271/) (2018)
- [A Survey on Computational Propaganda Detection](https://mlsanity.com/paper/2007.08024/) (2020)
- [The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race](https://mlsanity.com/paper/1701.03017/) (2017)
- [A Decade of Social Bot Detection](https://mlsanity.com/paper/2007.03604/) (2020)
- [One-Class Adversarial Nets for Fraud Detection](https://mlsanity.com/paper/1803.01798/) (2018)
- [MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models](https://mlsanity.com/paper/2009.01048/) (2020)
- [PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models](https://mlsanity.com/paper/2109.06777/) (2021)
- [TIES: Temporal Interaction Embeddings For Enhancing Social Media Integrity At Facebook](https://mlsanity.com/paper/2002.07917/) (2020)
- [Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection](https://mlsanity.com/paper/1904.05132/) (2019)
- [Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2010.16324/) (2020)
- [How Relevant is the Turing Test in the Age of Sophisbots?](https://mlsanity.com/paper/1909.00056/) (2019)
- [Social Bots: Human-Like by Means of Human Control?](https://mlsanity.com/paper/1706.07624/) (2017)

## Built on by (most cited first, 3 of 3)

- [Asymmetric Certified Robustness via Feature-Convex Neural Networks](https://mlsanity.com/paper/2302.01961/) (2023)
- [Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally](https://mlsanity.com/paper/2310.03614/) (2023)
- [Securing Educational LLMs: A Generalised Taxonomy of Attacks on LLMs and DREAD Risk Assessment](https://mlsanity.com/paper/2508.08629/) (2025)

## Abstract (arXiv)

Adversarial examples are inputs to a machine learning system that result in an incorrect output from that system. Attacks launched through this type of input can cause severe consequences: for example, in the field of image recognition, a stop signal can be misclassified as a speed limit indication.However, adversarial examples also represent the fuel for a flurry of research directions in different domains and applications. Here, we give an overview of how they can be profitably exploited as powerful tools to build stronger learning models, capable of better-withstanding attacks, for two crucial tasks: fake news and social bot detection.