Unsupervised detection of adversarial attacks
There’s growing concern about new security threats that arise from machine learning models becoming an important component of many critical applications. At the top of the list of threats are adversarial attacks, data samples that have been inconspicuously modified to manipulate the behavior of the targeted machine learning model.
Adversarial machine learning has become a hot area of research and the topic of talks and workshops at artificial intelligence conferences. Scientists are regularly finding new ways to attack and defend machine learning models.
A new technique developed by researchers at Carnegie Mellon University and the KAIST Cybersecurity Research Center employs unsupervised learning to address some of the challenges of current methods used to detect adversarial attacks. Presented at the Adversarial Machine Learning Workshop (AdvML) of the ACM Conference on Knowledge Discovery and Data Mining (KDD 2021), the new technique takes advantage of machine learning explainability methods to find out which input data might have gone through adversarial perturbation.
Read the full analysis on TechTalks.
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