ARTiFACIAL: Automated Reverse Turing test using FACIAL features

Web services designed for human users are being abused by computer programs (bots). The bots steal thousands of free e-mail accounts in a minute, participate in online polls to skew results, and irritate people by joining online chat rooms. These real-world issues have recently generated a new research area called human interactive proofs (HIP), whose goal is to defend services from malicious attacks by differentiating bots from human users. In this paper, we make two major contributions to HIP. First, based on both theoretical and practical considerations, we propose a set of HIP design guidelines that ensure a HIP system to be secure and usable. Second, we propose a new HIP algorithm based on detecting human face and facial features. Human faces are the most familiar object to humans, rendering it possibly the best candidate for HIP. We conducted user studies and showed the ease of use of our system to human users. We designed attacks using the best existing face detectors and demonstrated the challenge they presented to bots.