Security

An adversarial example is a specially crafted input designed to mislead AI models into making incorrect predictions.

Adversarial robustness refers to the ability of AI systems to withstand malicious inputs designed to deceive them.

Defense-GAN is a type of Generative Adversarial Network designed to enhance the security of machine learning models.

Luhn's Algorithm is a checksum formula used to validate identification numbers, particularly credit card numbers.

Membership inference is a type of attack that determines if a specific data point was used in training a machine learning model.

Model extraction is a process where an attacker recreates a machine learning model by querying it.

Model inversion is a technique used to extract sensitive data from machine learning models.

A method to extract sensitive data from machine learning models by exploiting their predictions.

Model poisoning is an attack that compromises machine learning models by introducing malicious data.

Rate limiting controls the number of requests a user can make to a service in a given time period to prevent abuse.

Soft targets are locations or individuals that are vulnerable to attacks due to their lack of security.

A token is a unit of digital data that represents something else in computing and cryptocurrency.