Machine learning is behind many of the systems we typically use, both online and offline, and behind even more of the systems we might use in the future. Given their economic importance, they attract attackers who might be interested in interfering with their correct behavior. Unfortunately, machine learning techniques introduce novel and potentially dangerous vulnerabilities that have not been at the forefront of machine learning research. At least this was the case until the advent of secure machine learning, a subfield that will likely increase in importance in the future.
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