Adversarial Machine Learning: Defense Strategies

Author(s): Michał Oleszak

Originally published on Towards AI.

Know thine enemy and protect your machine learning systems.

The growing prevalence of ML models in business-critical applications results in an increased incentive for malicious actors to attack the models for their benefit. Developing robust defense strategies becomes paramount as the stakes grow, especially in high-risk applications like autonomous driving and finance.

In this article, we’ll review common attack strategies and dive into the latest defense mechanisms for shielding machine learning systems against adversarial attacks. Join us as we unpack the essentials of safeguarding your AI investments.

“Know thine enemy” — this famous saying, derived from Sun Tzu’s The Art of War, an ancient Chinese military treatise, is just as applicable to machine-learning systems today as it was to 5th-century BC warfare.

Before we discuss defense strategies against adversarial attacks, let’s briefly examine how these attacks work and what types of attacks exist. We will also review a couple of examples of successful attacks.

An adversary is typically attacking your AI system for one of two reasons:

To impact the predictions made by the model.To retrieve and steal the model and/or the data it was trained on.

Attackers could introduce noise or misleading information into a model’s training data or inference input to alter its outputs.

The goal might be to bypass an ML-based security gate…. Read the full blog for free on Medium.

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Published via Towards AI