A variety of automated turing tests for network security: Using AI-hard problems in perception and cognition to ensure secure collaborations

There are a multitude of collaborative and network applications that are vulnerable to interference, infiltration, or attack by automated computer programs. Malicious programs can spam or otherwise disrupt email systems, blogs, and file sharing networks. They can cheat at online gaming, skew the results of online polls, or conduct denial-of-service attacks. And sophisticated AI ldquochat-botsrdquo can pose as humans in order to gather intelligence from unsuspecting targets. Thus, a recurring problem in collaborative systems is how to verify that a user is a human and not a computer. Following the work of Coates et al., von Ahn et al., and others, we propose several AI-hard problems in perception and cognition that can serve as ldquoCAPTCHAs,rdquo or tests capable of distinguishing between human-level intelligence and artificial intelligence, ensuring that all collaborators interfacing a particular system are humans and not nefarious computer programs.