TY - RPRT AU - El Ahmad, Ahmad S AU - Yan, Jeff AU - Tayara, Mohamad A2 - T1 - The Robustness of Google CAPTCHAs PB - School of Computer Science, Newcastle University, UK AD - PY - 2011/05 VL - IS - SP - EP - UR - http://homepages.cs.ncl.ac.uk/jeff.yan/google.pdf M3 - KW - captcha KW - character KW - google KW - image KW - ocr KW - recognition KW - segmentation L1 - N1 - N1 - N1 - AB - We report a novel attack on two CAPTCHAs that have been widely deployed on the Internet, one being Google's home design and the other acquired by Google (i.e. reCAPTCHA). With a minor change, our attack program also works well on the latest ReCAPTCHA version, which uses a new defence mechanism that was unknown to us when we designed our attack. This suggests that our attack works in a fundamental level. Our attack appears to be applicable to a whole family of text CAPTCHAs that build on top of the popular segmentation-resistant mechanism of "crowding character together" for security. Next, we propose a novel framework that guides the application of our well-tested security engineering methodology for evaluating CAPTCHA robustness, and we propose a new general principle for CAPTCHA design. ER - TY - CONF AU - Zhu, Bin B. AU - Yan, Jeff AU - Li, Qiujie AU - Yang, Chao AU - Liu, Jia AU - Xu, Ning AU - Yi, Meng AU - Cai, Kaiwei A2 - T1 - Attacks and design of image recognition CAPTCHAs T2 - CCS '10: Proceedings of the 17th ACM conference on Computer and communications security PB - ACM CY - New York, NY, USA PY - 2010/october M2 - VL - IS - SP - 187 EP - 200 UR - http://portal.acm.org/citation.cfm?id=1866307.1866329 M3 - 10.1145/1866307.1866329 KW - captcha KW - image KW - recognition KW - security KW - web L1 - SN - 978-1-4503-0245-6 N1 - N1 - AB - We systematically study the design of image recognition CAPTCHAs (IRCs) in this paper. We first review and examine all existing IRCs schemes and evaluate each scheme against the practical requirements in CAPTCHA applications, particularly in large-scale real-life applications such as Gmail and Hotmail. Then we present a security analysis of the representative schemes we have identified. For the schemes that remain unbroken, we present our novel attacks. For the schemes for which known attacks are available, we propose a theoretical explanation why those schemes have failed. Next, we provide a simple but novel framework for guiding the design of robust IRCs. Then we propose an innovative IRC called Cortcha that is scalable to meet the requirements of large-scale applications. It relies on recognizing objects by exploiting the surrounding context, a task that humans can perform well but computers cannot. An infinite number of types of objects can be used to generate challenges, which can effectively disable the learning process in machine learning attacks. Cortcha does not require the images in its image database to be labeled. Image collection and CAPTCHA generation can be fully automated. Our usability studies indicate that, compared with Google's text CAPTCHA, Cortcha allows a slightly higher human accuracy rate but on average takes more time to solve a challenge. ER - TY - CONF AU - Yan, Rong AU - Natsev, Apostol AU - Campbell, Murray A2 - T1 - An efficient manual image annotation approach based on tagging and browsing T2 - MS '07: Workshop on multimedia information retrieval on The many faces of multimedia semantics PB - ACM CY - New York, NY, USA PY - 2007/ M2 - VL - IS - SP - 13 EP - 20 UR - http://portal.acm.org/citation.cfm?id=1290071 M3 - http://doi.acm.org/10.1145/1290067.1290071 KW - annotation KW - browsing KW - image KW - manual KW - tagging L1 - SN - 978-1-59593-782-7 N1 - An efficient manual image annotation approach based on tagging and browsing N1 - AB - This paper investigates new approaches to improve the efficiency of manual image annotation and help users to produce better annotation results in a given amount of time. Although important in practice, this issue has rarely been studied in a quantitative way before. To achieve this, we first propose two time models to analyze the annotation process for two popular manual annotation approaches, i.e., tagging and browsing. The complementary properties of these approaches have inspired us to merge them to develop a hybrid annotation algorithms called frequency-based annotation. Our experiments on large-scale multimedia collections have shown that the proposed algorithm can achieve an up to 40% annotation time reduction compared with the baseline methods. In other words, it can produce considerably better results using the same annotation time. ER - TY - CONF AU - von Ahn, Luis AU - Dabbish, Laura A2 - T1 - Labeling images with a computer game T2 - CHI '04: Proceedings of the SIGCHI conference on Human factors in computing systems PB - ACM CY - New York, NY, USA PY - 2004/ M2 - VL - IS - SP - 319 EP - 326 UR - http://portal.acm.org/citation.cfm?id=985733 M3 - http://doi.acm.org/10.1145/985692.985733 KW - annotation KW - game KW - image KW - labeling L1 - SN - 1-58113-702-8 N1 - N1 - AB - We introduce a new interactive system: a game that is fun and can be used to create valuable output. When people play the game they help determine the contents of images by providing meaningful labels for them. If the game is played as much as popular online games, we estimate that most images on the Web can be labeled in a few months. Having proper labels associated with each image on the Web would allow for more accurate image search, improve the accessibility of sites (by providing descriptions of images to visually impaired individuals), and help users block inappropriate images. Our system makes a significant contribution because of its valuable output and because of the way it addresses the image-labeling problem. Rather than using computer vision techniques, which don't work well enough, we encourage people to do the work by taking advantage of their desire to be entertained. ER - TY - BOOK AU - Lynch, Kevin A2 - T1 - The image of the city PB - MIT Press AD - PY - 1992/ VL - IS - SP - EP - UR - http://books.google.de/books?id=\_phRPWsSpAgC M3 - KW - city KW - geo KW - geography KW - image KW - map KW - mapping KW - toread L1 - SN - 9780262620017 N1 - N1 - AB - ER -