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By Kumar Chellapilla, Kevin Larson, Patrice Y. Simard, Mary Czerwinski (auth.), Henry S. Baird, Daniel P. Lopresti (eds.)
This booklet constitutes the refereed lawsuits of the second one foreign Workshop on Human Interactive Proofs, HIP 2005, held in Bethlehem, PA, united states in may well 2005.
The nine revised complete papers provided have been conscientiously reviewed and chosen for presentation. This ebook is the 1st archival ebook dedicated to the recent type of safeguard protocols referred to as human interactive proofs. The version of HIPs getting used in e-commerce and internet companies at the present time is totally computerized public Turing assessments to inform desktops and people aside, CAPTCHAs. The papers during this ebook are geared up in topical sections on CAPTCHAs and function research, HIP architectures, HIP as safeguard platforms components.
Read or Download Human Interactive Proofs: Second International Workshop, HIP 2005, Bethlehem, PA, USA, May 19-20, 2005. Proceedings PDF
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We should explain that most of the images correctly recognized by our handwriting recognizers are falling in this category. Fig. 9 shows several images that were correctly recognized but where the transformation chosen did not modify the original image too much and therefore did not add enough challenge to the recognition task. But on the other hand we have seen that for fairly clean images with well chosen parameters for transformations the recognizers would have difficulties (Fig. 3 through Fig.
4. ScatterType challenges rated by subjects as being of medium difficulty (difficulty level 3 out of 5). Only one of these examples was read correctly (correct/attempt): “ovorch”/”overch”, “wouwould”, “adager”/“atlager”, “weland”/”wejund”. A Highly Legible CAPTCHA That Resists Segmentation Attacks 31 Fig. 5. ScatterType challenges rated by subjects as “Impossible” (difficulty level 5 out of 5). 7 Fig. 6. Human reading performance as a function of the difficulty level that the subject selected. 5 A Highly Legible Regime We have systematically explored the improvements in legibility that can be expected from judicious choices of generating parameters (distributions that control cutting and scattering).
We have learned that decomposing the HIP problem into segmentation and recognition greatly simplifies analysis. Recognition on even unprocessed images (given segmentation is solved) can be done automatically using neural networks. Further, the HIP user studies have shown that given correct segmentation, computers are much better at HIP character recognition than humans. Segmentation, on the other hand, is the difficulty differentiator between weaker and stronger HIPs. Preliminary user studies on segmentation based HIP indicate that humans are just as good at solving segmentation based HIPs as they are at solving recognition based HIPs.