Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Interactive Proofs and Document Image Analysis
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Pessimal Print: A Reverse Turing Test
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
ARTiFACIAL: automated reverse turing test using FACIAL features
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
CAPTCHA: using hard AI problems for security
EUROCRYPT'03 Proceedings of the 22nd international conference on Theory and applications of cryptographic techniques
Recognizing objects in adversarial clutter: breaking a visual captcha
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Embedded noninteractive continuous bot detection
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
SEMAGE: a new image-based two-factor CAPTCHA
Proceedings of the 27th Annual Computer Security Applications Conference
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Web services designed for human users are being abused by computer programs (bots). The bots steal thousands of free email 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. 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 difficulty to bots.