Work on adversarial machine learning has yielded results that range from the funny, benign, and embarrassing—such as to following turtle being mistaken for a rifle—to potentially harmful ...

Adversarial attacks that only need access to the output of a machine learning model are “black box attacks.” PACD stands somewhere in between the two ends of the spectrum.

Adversarial machine learning doesn’t pose an immediate threat in the future. However, cybersecurity researchers are concerned that as ML and AI are integrated into a broader array of our everyday ...

Work on adversarial machine learning has yielded results that range from the funny, benign, and embarrassing—such as to following turtle being mistaken for a rifle—to potentially harmful ...

In terms of adversarial machine learning, this could mean cybersecurity vendors get hacked themselves by threat actors looking to gain access to the algorithms and data that trains their models.

The vulnerabilities of machine learning models open the door for deceit, giving malicious operators the opportunity to interfere with the calculations or decision making of machine learning systems.

The Adversarial ML Threat Matrix comes with a set of case studies of attacks that involve traditional security vulnerabilities, adversarial machine learning, and combinations of both.

What should people new to the field know about adversarial machine learning? originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better ...

An important milestone in adversarial defenses took place recently. Microsoft, MITRE, and 11 other organizations released an Adversarial ML Threat Matrix.

Machine learning, for all its benevolent potential to detect cancers and create collision-proof self-driving cars, also threatens to upend our notions of what's visible and hidden. It can, for ...