Adversarial Training

Adversarial training is a machine learning technique used to improve the robustness of models, particularly neural networks, against adversarial attacks. It involves training a model on both clean data and adversarially perturbed examples, where small, often imperceptible, modifications are made to input data to cause misclassification. This process helps the model learn to be more resilient to such manipulations, enhancing its security and reliability in real-world applications.

Also known as: Adversarial Robustness Training, Adversarial Defense Training, AT, Robust Training, Adversarial Learning

Why learn Adversarial Training?

Developers should learn adversarial training when building machine learning models for security-critical applications, such as autonomous vehicles, fraud detection, or facial recognition systems, where robustness against malicious inputs is essential. It is particularly valuable in domains like computer vision and natural language processing to defend against evasion attacks that exploit model vulnerabilities. By implementing adversarial training, developers can create more trustworthy AI systems that perform reliably even under adversarial conditions.

Learning Resources

• tutorial: Adversarial Machine Learning at MIT
• docs: Explaining and Harnessing Adversarial Examples (Paper)
• course: Adversarial Robustness - Theory and Practice
• video: Adversarial Training Explained - YouTube
• book: Adversarial Machine Learning Book