Adversarial Machine Learning: Bayesian Perspectives

Journal of the American Statistical Association, 2023


Adversarial Machine Learning (AML) is a growing field focused on defending Machine Learning (ML) systems from security threats where adversaries manipulate input data to deceive these systems. 

While most AML work relies on game-theoretic modeling of the conflict between an ML system and its adversary, this approach makes unrealistic assumptions about each party’s knowledge of the other’s intentions and uncertainties.  

This paper proposes a Bayesian approach that allows for better modeling of uncertainties regarding an opponent’s beliefs and interests, offering a more realistic and robust defensive method for ML-based systems. 

A Bayesian approach to enhance defense against security threats in Machine Learning systems, applicable across cybersecurity, unsupervised learning and natural language processing.

Where does it apply?

The Bayesian approach to Adversarial Machine Learning is applicable in enhancing cybersecurity by protecting machine learning systems from attacks.  It can be used in unsupervised learning to improve methods such as clustering, and in natural language processing to create robust language models.  

Other applications include fake news detection, development of robust algorithms for autonomous driving systems, improving counterfactual inference in observational studies, and making deep neural networks more robust for image classification. 

In short, it can be applied anywhere that uses machine learning systems for decision-making.

Why does it matters?

Adversarial Machine Learning (AML) matters because it is crucial for protecting ML algorithms, which are widely used in contemporary technology applications. These systems can be manipulated by adversaries posing security threats, thus, maintaining their robustness against attacks is essential. 

Moreover, the exploration of this approach can benefit various ML problems, leading to advancements in fields such as unsupervised learning, natural language processing, and reinforcement learning. 

The framework also has direct applications in defending against fake news, developing robust algorithms, improving observational studies, and enhancing adversarial robustness.

Adversarial Machine Learning: Bayesian Perspectives

Journal of the American Statistical Association, 2023

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