Breaking the PayPal HIP: A Comparison of classifiers

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Title: Breaking the PayPal HIP: A Comparison of classifiers
Author: Kluever, Kurt Alfred
Abstract: Human Interactive Proofs (HIPs) are a method used to differentiate between humans and machines on the internet. Providers of online services such as use HIPs to prevent automated signups and abuse of their services. In this experiment, a three step algorithm has been developed to break the HIP. The image is preprocessed to remove noise using thresholding and a simple cleaning technique, and then segmented using vertical projections and candidate split positions. Four classification methods have been implemented: pixel counting, vertical projections, horizontal projections and template correlations. The system was trained on a sample of twenty HIPs to create thirty-six training templates (one for each character: 0-9 and A-Z). A sample of 100 HIPs were used for testing. The following HIP success rates have been achieved using the different classifiers: 8% pixel counting, vertical projections 97%, horizontal projections 100%, template correlations 100%. Three of the classifiers out perform the 88% HIP success rate of [6].
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Date: 2008

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