ARTiFACIAL: Automated Reverse Turing test using FACIAL features

Web services designed for human users are being abused by computer programs (bots). The bots stealfl

thousands of free email accounts in a minute; participate in online polls to skew results; and irritate people byfl

joining online chat rooms. These real-world issues have recently generated a new research area called Humanfl

Interactive Proofs (HIP), whose goal is to defend services from malicious attacks by differentiating bots fromfl

human users. In this paper, we make two major contributions to HIP. First, based on both theoretical andfl

practical considerations, we propose a set of HIP design guidelines which ensure a HIP system to be secure andfl

usable. Second, we propose a new HIP algorithm based on detecting human face and facial features. Humanfl

faces are the most familiar object to humans, rendering it possibly the best candidate for HIP. We conducted userfl

studies and showed the ease of use of our system to human users. We designed attacks using the best existingfl

face detectors and demonstrated the difficulty to bots.

2004-zliu-mmsj.pdf
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In  ACM Multimedia Systems Journal (Springer). Vol 9, No. 6, pp. 493 - 502

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