The term \u201eAI robustness testing\u201c is primarily found in the fields of Artificial Intelligence, cybercrime and cybersecurity, as well as industry and Industry 4.0. <\/p>\n
Adversarial testing for AI describes methods used to check how susceptible an AI system is to errors or manipulation. The aim is to find out if and how easily artificial intelligence can be tricked or disrupted. This is particularly important as many companies use AI in sensitive areas such as production, security, or quality control.<\/p>\n
A simple example: A company uses an AI-powered camera to detect faulty products on a conveyor belt. A failure mode testing is then carried out to check if the AI overlooks small changes to the products or perhaps makes wrong decisions when the lighting changes or dust gets onto the lens.<\/p>\n
These tests can uncover vulnerabilities before they lead to serious problems or security risks in day-to-day operations. Fuzzing tests for AI thus help companies to improve the reliability and security of their systems before they deploy them on a larger scale.<\/p>","protected":false},"excerpt":{"rendered":"