Artificial intelligence tools are more susceptible to targeted attacks than previously anticipated, effectively forcing AI systems to make poor choices.
The term “adversarial attacks” refers to the manipulation of data being fed into an AI system in order to create confusion in the system. For example, someone might know that putting a specific type of sticker at a specific spot on a stop sign could effectively make the stop sign invisible to an AI system. Hackers can also install code on an X-ray machine that alters image data, leading an AI system to make inaccurate diagnoses.
“For the most part, you can make all sorts of changes to a stop sign, and an AI that has been trained to identify stop signs will still know it’s a stop sign,” stated Tianfu Wu, coauthor of a paper on the new work and an associate professor of electrical and computer engineering at North Carolina State University. “However, if the AI has a vulnerability, and an attacker knows the vulnerability, the attacker could take advantage of the vulnerability and cause an accident.”
Wu and his colleagues’ latest study aims to de
[…]
Content was cut in order to protect the source.Please visit the source for the rest of the article.
[…]
Content was cut in order to protect the source.Please visit the source for the rest of the article.
This article has been indexed from CySecurity News – Latest Information Security and Hacking Incidents
Read the original article: