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Detecting fake accounts in online social networks (OSNs) protects OSN operators and their users from various malicious activities. Most detection mechanisms attempt to predict and classify user accounts as real (ie, benign, honest) or fake (ie, malicious, Sybil) by analyzing user-level activities or graph-level structures. These mechanisms, however, are not robust against adversarial attacks in which fake accounts cloak their operation with patterns resembling real user behavior.

We herein demonstrate that victims, benign users who control real accounts and have befriended fakes, form a distinct classification category that is useful for designing robust detection mechanisms. First, as attackers have no control over victim accounts and cannot alter their activities, a victim account classifier which relies on user-level activities is relatively harder to circumvent. Second, as fakes are directly connected to victims, a fake account detection mechanism that integrates victim prediction into graphlevel structures is more robust against manipulations of the graph.