University of California

Mohammad Samragh Razlighi

Farinaz Koushanfar

Bita Darvish Rouhani

Tara Javidi

A method for detecting and/or preventing an adversarial attack against a target machine learning model may be provided. The method may include training, based at least on training data, a defender machine learning model to enable the defender machine learning model to identify malicious input samples. The trained defender machine learning model may be deployed at the target machine learning model. The trained defender machine learning model may be coupled with the target machine learning model to at least determine whether an input sample received at the target machine learning model is a malicious input sample and/or a legitimate input sample. Related systems and articles of manufacture, including computer program products, are also provided.

Growing community of inventors

San Diego, CA, United States of America

Mohammad Samragh Razlighi