PessimalPrint: a reverse Turing test
PessimalPrint: a reverse Turing test
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DOI：
10.1007/s10032-002-0089-1
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发表时间：
2001-09
影响因子：
2.3
通讯作者：
H. Baird;Allison L. Coates;R. Fateman
中科院分区：
文献类型：
--
作者：
H. Baird;Allison L. Coates;R. Fateman
摘要：我们利用人类和机器视觉系统之间的能力差距，设计了一系列自动挑战，通过包括互联网浏览器在内的图形界面区分人类和机器用户。图灵[Tur50]提出了一种方法，通过这种方法，人类法官可以通过无法区分人类和机器对话者来验证“人工智能”。受到雅虎的Udi Manber提出的“聊天室问题”的刺激，并受到Manuel Blum等人的验证码项目[BAL00]的影响。在卡内基-梅隆大学，我们提出了一种使用悲观印刷体的图灵测试的变体：即机器印刷文本的低质量图像，在一定范围的单词、字体和图像降级上伪随机合成。我们的实验表明，明智地选择这些范围可以确保图像对于人类读者来说是可读的，但对于当今最好的几种光学字符识别(OCR)机器来说是难以辨认的。我们的方法的动机是十年来对OCR机器的性能评估[RJN96，RNN99]和文档图像质量的定量随机模型[Bai92，Kan96]的研究。几十年来，OCR和其他种类的机器视觉进化的缓慢步伐[NS96，Pav00]表明，悲观的印刷体将在未来许多年里抵抗自动攻击。应用包括“BOT”壁垒和数据库配给。
Abstract.We exploit the gap in ability between human and machine vision systems to craft a family of automatic challenges that tell human and machine users apart via graphical interfaces including Internet browsers. Turing proposed [Tur50] a method whereby human judges might validate “artificial intelligence” by failing to distinguish between human and machine interlocutors. Stimulated by the “chat room problem” posed by Udi Manber of Yahoo!, and influenced by the CAPTCHA project [BAL00] of Manuel Blum et al. of Carnegie-Mellon Univ., we propose a variant of the Turing test usingpessimal print: that is, low-quality images of machine-printed text synthesized pseudo-randomly over certain ranges of words, typefaces, and image degradations. We show experimentally that judicious choice of these ranges can ensure that the images are legible to human readers but illegible to several of the best present-day optical character recognition (OCR) machines. Our approach is motivated by a decade of research on performance evaluation of OCR machines [RJN96,RNN99] and on quantitative stochastic models of document image quality [Bai92,Kan96]. The slow pace of evolution of OCR and other species of machine vision over many decades [NS96,Pav00] suggests that pessimal print will defy automated attack for many years. Applications include `bot' barriers and database rationing.