# Adversarial Machine Learning: Bayesian Perspectives

arXiv: https://arxiv.org/abs/2003.03546 · HTML: https://mlsanity.com/paper/2003.03546/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [The last Dance : Robust backdoor attack via diffusion models and bayesian approach](https://mlsanity.com/paper/2402.05967/) (2024)
- [Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approach](https://mlsanity.com/paper/2406.10719/) (2024)
- [Poisoning Bayesian Inference via Data Deletion and Replication](https://mlsanity.com/paper/2503.04480/) (2025)
- [A Framework for Rapidly Developing and Deploying Protection Against Large Language Model Attacks](https://mlsanity.com/paper/2509.20639/) (2025)
- [Evasion Attacks Against Bayesian Predictive Models](https://mlsanity.com/paper/2506.09640/) (2025)
- [Color Teams for Machine Learning Development](https://mlsanity.com/paper/2110.10601/) (2021)
- [Manipulating hidden-Markov-model inferences by corrupting batch data](https://mlsanity.com/paper/2402.13287/) (2024)
- [Adversarial observations in probabilistic State-Space Models for robust Reinforcement Learning](https://mlsanity.com/paper/2606.20880/) (2026)

## Abstract (arXiv)

Adversarial Machine Learning (AML) is emerging as a major field aimed at protecting machine learning (ML) systems against security threats: in certain scenarios there may be adversaries that actively manipulate input data to fool learning systems. This creates a new class of security vulnerabilities that ML systems may face, and a new desirable property called adversarial robustness essential to trust operations based on ML outputs. Most work in AML is built upon a game-theoretic modelling of the conflict between a learning system and an adversary, ready to manipulate input data. This assumes that each agent knows their opponent's interests and uncertainty judgments, facilitating inferences based on Nash equilibria. However, such common knowledge assumption is not realistic in the security scenarios typical of AML. After reviewing such game-theoretic approaches, we discuss the benefits that Bayesian perspectives provide when defending ML-based systems. We demonstrate how the Bayesian approach allows us to explicitly model our uncertainty about the opponent's beliefs and interests, relaxing unrealistic assumptions, and providing more robust inferences. We illustrate this approach in supervised learning settings, and identify relevant future research problems.