Adversarial Defense

Published:

Adversarial defense is part of AI security and focuses on protecting models from inputs that are deliberately crafted to fool them. These attacks might try to make a model misclassify something, poison its training data, or reveal information about how the model works. The first step in building defenses is understanding the threat: who might attack and what access they have.

To defend against these risks, teams use several strategies. They may train the model on intentionally altered examples so it becomes more resistant, add checks that clean or normalize inputs, design more robust architectures, or build monitoring tools that detect suspicious patterns in model usage. Because attackers continually adapt, defenses often include multiple layers and are updated regularly.

Follow us on Facebook and LinkedIn to keep abreast of our latest news and articles