Asirra: a CAPTCHA that exploits interest-aligned manual image categorization
Asirra: a CAPTCHA that exploits interest-aligned manual image categorization
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DOI：
10.1145/1315245.1315291
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发表时间：
2007-10
期刊：
影响因子：
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通讯作者：
J. Elson;J. Douceur;Jon Howell;J. Saul
中科院分区：
文献类型：
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作者：
J. Elson;J. Douceur;Jon Howell;J. Saul
我们展示了Asirra（图1），这是一个CAPTCHA，要求用户从一组12张猫和狗的照片中识别猫。Asirra对用户来说很容易;用户研究表明，人类可以在30秒内解决99.6%的问题。除非机器视觉取得重大进展，否则我们预计计算机解决这个问题的机会不会超过1/54,000。Asirra的图像数据库是由与Petfinder.com建立的新型互利合作伙伴关系提供的。作为使用他们300万张照片的交换，我们在每张照片下面都显示了一个“收养我”的链接，宣传Petfinder的主要使命，那就是为无家可归的动物寻找家园。我们描述Asirra的设计，讨论其安全威胁，并报告早期部署经验。我们还描述了两种新的算法，用于放大人类和计算机之间的技能差距，可以用于许多现有的CAPTCHA。
We present Asirra (Figure 1), a CAPTCHA that asks users to identify cats out of a set of 12 photographs of both cats and dogs. Asirra is easy for users; user studies indicate it can be solved by humans 99.6% of the time in under 30 seconds. Barring a major advance in machine vision, we expect computers will have no better than a 1/54,000 chance of solving it. Asirra’s image database is provided by a novel, mutually beneficial partnership with Petfinder.com. In exchange for the use of their three million images, we display an “adopt me” link beneath each one, promoting Petfinder’s primary mission of finding homes for homeless animals. We describe the design of Asirra, discuss threats to its security, and report early deployment experiences. We also describe two novel algorithms for amplifying the skill gap between humans and computers that can be used on many existing CAPTCHAs.