Evasion Attacks

Evasion attacks are adversarial techniques in machine learning and cybersecurity where an attacker deliberately crafts inputs to deceive a model into making incorrect predictions while appearing normal to human observers. These attacks exploit vulnerabilities in model decision boundaries, often by adding small, imperceptible perturbations to data such as images, text, or audio. They are a key focus in adversarial machine learning, highlighting security risks in AI systems.

Also known as: Adversarial Evasion, Evasion Techniques, Adversarial Attacks, Model Evasion, ML Evasion

Why learn Evasion Attacks?

Developers should learn about evasion attacks when building or deploying machine learning models in security-critical applications like autonomous vehicles, fraud detection, or malware classification, as these attacks can compromise system reliability and safety. Understanding evasion techniques helps in designing robust models, implementing defenses such as adversarial training, and ensuring compliance with security standards in industries like finance and healthcare.

Learning Resources

• docs: Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations
• tutorial: Explaining and Harnessing Adversarial Examples (Goodfellow et al.)
• course: Adversarial Attacks and Defenses in Deep Learning on Coursera
• video: Adversarial Machine Learning Explained by Two Minute Papers
• book: The Security of Machine Learning Book