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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/32736
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/32736

    Title: 植基於圖像內涵之自動化人機區分機制
    A CAPTCHA Mechanism By Exchanging Image Blocks
    Authors: 廖奕齊
    Liao,I Chi
    Contributors: 廖文宏
    Liao,Wen Hung
    Liao,I Chi
    Keywords: 人機區分
    Date: 2006
    Issue Date: 2009-09-17 14:10:03 (UTC+8)
    Abstract: 由於自動化程式的濫用越來越廣泛,因此擁有區分人與機器的能力也就日益重要。然而現在廣泛被運用的原文圖像(textual-image-based)CAPTCHA已經遭到破解。在此篇論文中,我們提出一個以交換圖片中不重疊區塊、簡單且有效的人機區分機制,利用簡單的幾個步驟就能產生出人類可以輕鬆通過但機器卻難以用自動化程式分析的測驗圖片,也同時針對此機制的強健度做了多方面的測試,實驗中也對於此機制所使用的參數選擇和圖像資料庫進行詳細的分析;最後我們設計了眼動儀實驗去比較不同的測驗類型所對應的視線軌跡。
    The need to tell human and machines apart has surged due to abuse of automated ‘bots’. However, several textual-image-based CAPTCHAs have been defeated recently. In this thesis, we propose a simple yet effective visual CAPTCHA test by exchanging the content of non-overlapping regions in an image. Using simple steps, the algorithm is able to produce a discrimination mechanism which is difficult for machine to analyze but easy for human to pass. We have tested the robustness of the proposed method by exploring different ways to attack this CAPTCHA and the corresponding counter-attack measures. Additionaly, we have carried out in-depth analysis regarding the choice of the parameters and the image database. Finally, eye-tracking experiments have been conducted to examine and compare the gaze paths for different visual tasks.
    Reference: 【1】 Ahn, L von., Blum, M., Hopper, N. J., and Langford, J., “CAPTCHA: Telling Humans and Computers Apart (Automatically)”, Advances in Cryptology, Eurocrypt `03, Vol. 2656 of Lecture Notes in Computer Science, pp.294–311, 2003.
    【2】 Turing,A. (1950). Computing machinery and intelligence, artificial intelligence.
    【3】 Liao, W. H., Chang, C.C. “Embedding Information within Dynamic Visual Patterns”, Proceedings of IEEE International Conference On Multimedia And Expo, May 2004.
    【4】 張繼志,植基於質感圖像之自動化人機區分機制,國立政治大學資訊科學研究所,民國94年。
    【5】 Rusu A. and Govindaraju V. , “Handwritten CAPTCHA: Using the difference in the abilities of humans and machines in reading handwritten words”, Proceedings of the 9th Int’l Workshop on Frontiers in Handwriting Recognition, Sept 2004.
    【6】 Misra D. and Gaj K. , “Face Recognition CAPTCHAs” Proceedings of the Advanced International Conference on Telecommunications and International Conference on Internet and Web Applications and Services, 2006
    【7】 Chakrabarti S. and Singhal M. , “Password-Based Authentication: Preventing Dictionary Attacks”, Proceeing of IEEE Computer, June 2007.
    【8】 http://research.microsoft.com/asirra/
    【9】 http://www.w3.org/TR/turingtest/
    【10】 Moscovitch M., Winocur G. and Behrmann M., “What Is Special about Face Recognition?: Nineteen Experiments on a Person with Visual Object Agnosia and Dyslexia but Normal Face Recognition“, The Journal of Cognitive Neuroscience, Vol. 9, pp. 555-604, 1997.
    【11】 Martin D., Fowlkes C. and Malik. J., "Learning to Detect Natural Image Boundaries Using Local Brightness, Color and Texture Cues", IEEE Transactions on Pattern Analysis and Machine Intelligence, 26 (5) p.530-549, 2004.
    【12】 http://www.seas.upenn.edu/~timothee/
    【13】 Dirk Walther and Christof Koch, Modeling attention to salient proto-objects. Neural
    Networks (2006) 19, 1395-1407 (http://www.saliencytoolbox.net)
    【14】 Michael M. , Mads Larsen, Janusz Sierakowski, Mikkel B. S , “The IMM Face
    Database - An Annotated Dataset of 240 Face Images" Informatics and Mathematical Modelling, Technical University of Denmark, DTU , May 2004
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0947530342
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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