Adversarial Attack Detection

Adversarial attack detection refers to the identification and defense against malicious inputs targeting machine learning models, which are designed to mislead the models into producing incorrect outputs. The goal of this task is to timely detect and prevent adversarial attacks by analyzing the abnormal characteristics of input data, thereby enhancing the security and robustness of the models. With the support of a knowledge base, adversarial attack detection can be widely applied in fields such as cybersecurity and financial risk control, ensuring the reliable operation of systems.