[HTML][HTML] Sensitivity of machine learning approaches to fake and untrusted data in healthcare domain
Machine Learning models are susceptible to attacks, such as noise, privacy invasion, replay,
false data injection, and evasion attacks, which affect their reliability and trustworthiness …
false data injection, and evasion attacks, which affect their reliability and trustworthiness …
Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain.
F Marulli, S Marrone, L Verde - Journal of Sensor & Actuator …, 2022 - search.ebscohost.com
Abstract Machine Learning models are susceptible to attacks, such as noise, privacy
invasion, replay, false data injection, and evasion attacks, which affect their reliability and …
invasion, replay, false data injection, and evasion attacks, which affect their reliability and …
Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain
F Marulli, S Marrone, L Verde - JOURNAL OF SENSOR AND …, 2022 - iris.unicampania.it
Abstract Machine Learning models are susceptible to attacks, such as noise, privacy
invasion, replay, false data injection, and evasion attacks, which affect their reliability and …
invasion, replay, false data injection, and evasion attacks, which affect their reliability and …
Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain
F Marulli, S Marrone, L Verde - Journal of Sensor and Actuator …, 2022 - search.proquest.com
Abstract Machine Learning models are susceptible to attacks, such as noise, privacy
invasion, replay, false data injection, and evasion attacks, which affect their reliability and …
invasion, replay, false data injection, and evasion attacks, which affect their reliability and …