Adversarial attacks refer to deliberate and malicious attempts to deceive or manipulate machine learning models.

Adversarial attacks aim to exploit vulnerabilities in machine learning models to make incorrect or unintended predictions.

Attackers create adversarial examples by subtly modifying input data to mislead the model.

Adversarial attacks can target various types of ML models, including image classifiers, NLP models, and anomaly detection systems.

Common types of adversarial attacks include evasion attacks, poisoning attacks, and model inversion attacks.

Adversarial attacks can be successful even when the attacker has limited knowledge about the targeted model's architecture

Adversarial attacks can have real-world consequences, such as fooling autonomous vehicles into misinterpreting road signs or bypassing security systems.

Adversarial attacks are an ongoing research area, and new defense mechanisms and attack techniques continue to emerge.