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Pattern classification systems are commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering, in which data can be purposely manipulated by humans to undermine their operation. As this adversarial scenario is not taken into account by classical design methods, pattern classification systems may exhibit vulnerabilities, whose exploitation...
In adversarial classification tasks like spam filtering, intrusion detection in computer networks, and biometric identity verification, malicious adversaries can design attacks which exploit vulnerabilities of machine learning algorithms to evade detection, or to force a classification system to generate many false alarms, making it useless. Several works have addressed the problem of designing robust...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.