# Towards Resilient Artificial Intelligence: Survey and Research Issues

arXiv: https://arxiv.org/abs/2109.08904 · HTML: https://mlsanity.com/paper/2109.08904/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 67 references that are in the map.
- First posted on arXiv 18 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Adversarial Attacks on Neural Network Policies](https://mlsanity.com/paper/1702.02284/) (2017)
- [Do Adversarially Robust ImageNet Models Transfer Better?](https://mlsanity.com/paper/2007.08489/) (2020)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [Adversarial Policies: Attacking Deep Reinforcement Learning](https://mlsanity.com/paper/1905.10615/) (2019)
- [Robust Deep Reinforcement Learning with Adversarial Attacks](https://mlsanity.com/paper/1712.03632/) (2017)
- [Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks](https://mlsanity.com/paper/1701.04143/) (2017)
- [Secure and Robust Machine Learning for Healthcare: A Survey](https://mlsanity.com/paper/2001.08103/) (2020)
- [Data Poisoning Attack against Unsupervised Node Embedding Methods](https://mlsanity.com/paper/1810.12881/) (2018)
- [Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications](https://mlsanity.com/paper/2103.16910/) (2021)
- [CopyCAT: Taking Control of Neural Policies with Constant Attacks](https://mlsanity.com/paper/1905.12282/) (2019)
- [Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy](https://mlsanity.com/paper/1911.03849/) (2019)
- [Adversarial Attacks on Optimization based Planners](https://mlsanity.com/paper/2011.00095/) (2020)
- ["Part Man, Part Machine, All Cop": Automation in Policing](https://mlsanity.com/paper/2106.12794/) (2021)

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

- [Cooperative Resilience in Artificial Intelligence Multiagent Systems](https://mlsanity.com/paper/2409.13187/) (2024)
- [SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2301.04299/) (2023)

## Abstract (arXiv)

Artificial intelligence (AI) systems are becoming critical components of today's IT landscapes. Their resilience against attacks and other environmental influences needs to be ensured just like for other IT assets. Considering the particular nature of AI, and machine learning (ML) in particular, this paper provides an overview of the emerging field of resilient AI and presents research issues the authors identify as potential future work.