學術產出-學位論文

題名 植基於質感圖樣之自動化人機區分機制
A CAPTCHA Mechanism Based on Textured Patterns
作者 張繼志
Chi-Chih Chang
貢獻者 廖文宏
Wen-Hung Liao
張繼志
Chi-Chih Chang
關鍵詞 人機辨識
質感圖樣
CAPTCHA
Turning Test
Texture
Visual Pattern
日期 2004
上傳時間 17-九月-2009 14:06:36 (UTC+8)
摘要 隨著科技的進步與資訊科學的發展,大量的資訊處理自動化逐漸取代傳統人工技術,然而不恰當地使用自動化技術,卻可能危害人類的權益與空間。為避免過度濫用機器自動化對人類所造成的災害,本研究根據不同的適用情境,分別提出以靜態及動態圖型為基礎的人機區分方法,透過簡單的影像處理技術,產生機器難以分析但人類能夠易於判別的人機辨識影像。並且由認知的角度,設計實驗進一步探討人類視覺優勢以及接受度,作為影像產生時的標準。最後,提出人機區分技術與應用情境整合實作的方法,以觀實效。
The idea of using a computer program to distinguish humans from machines, sometimes referred to as the “Reverse Turing Test”, has emerged only quite recently. The term CAPTCHA, which stands for “Completely Automated Public Turing Test to Tell Computers and Humans Apart", is defined as:
“a program that can generate and grade tests that:
□ Most human can pass
but
□ Current computer program can’t pass! “
In this thesis, a texture-image based approach is developed to encode text information in such a way that machine vision algorithms will experience significant difficulties while human can extract the embedded text effortlessly. Both static images and dynamic sequences will be explored. It is anticipated that the cost of storing, and subsequently decoding information from such visual patterns will be prohibitedly high, both in terms of time and space complexity. To validate the postulation, fundamental principles of the human cognitive process will be examined. Experiments will also be carried out to gather user feedback and investigate the limitations of human visual systems. Finally, several application scenarios that call for the integration of a CAPTCHA will be identified and discussed.
參考文獻 【1】Berners-Lee, T., Hendler, J. and Lassila, O. (2001). The Semantic Web. Scientific American.
【2】Turing, A. (1950). Computing machinery and intelligence, artificial intelligence.
【3】Ahn, L von., Blum, M., Hopper, N. J., and Langford, J. (2003). CAPTCHA: Telling Humans and Computers Apart (Automatically). Advances in Cryptology, Eurocrypt `03, volume 2656 of Lecture Notes in Computer Science, 294–311.
【4】Mori, G. and Malik, J. (2003). Recognizing objects in adversarial clutter: Breaking a visual CAPTCHA. In Proceedings of the Conference on Computer Vision and Pattern Recognition,. Vol. I, pp.134-141, Madison, USA.
【5】Kochanski, G., Lopresti, D., and Shih, C. (2002). A Reverse Turing Test Using Speech. Seventh International Conference on Spoken Language Processing, 16-20.
【6】Julesz, B. and Miller, J.E. (1962). Automatic stereoscopic presentation of functions of two variables. Bell System Technical Journal, 41: 663-676.
【7】Goldstein, E. B. (1999). Sensation and Perception, Fifth Edition, Brooks/Cole Publishing Company.
【8】Kanizsa, G.. (1955). Margini quasi-percettivi in campi con stimolazione omogenea. Rivista di psicologia, 49, 7-30.
【9】Bradley, D. R., and Petry, H. M. (1977). Organizational determinants of subjective contour: The subjective Necker cube. American Journal of Psychology, 90, 253-262.
【10】Williams, L. R. and Jacobs, D. W. (1997). Stochastic Completion Fields: A Neural odel of Illusory Contour Shape and Salience, Neural Computation, Vol. 9, No. 4, pp. 837-858.
【11】Julesz, B. (1962). Visual pattern discrimination. IRE Trans Inf Theory, IT-8:84-92.
【12】Julesz, B. (1975). Experiments on the visual perception of texture. Scientific American, 232, 34-43.
【13】Julesz, B. Textons. (1981). the elements of texture perception and their interactions. Nature, London, 290, 91-97.
【14】Regan, D. (1986). Luminance contrast: Vernier discrimination. Spatial Vision, 1, 305-318.
【15】Newsome, W. T., Britten, K. H., and Movshon, J. A. (1989). Neuronal correlates of a perceptual decision. Nature, 341, 52-54.
【16】Gonzalez, R. C. and Woods, R. E. (2001). Digital Image Processing Second Edition. Prentice-Hall, Inc.
【17】Forsyth, D. A.,and Ponce, J. (2003). Computer Vision: A Modern Approach. Prentice-Hall, Inc.
【18】Myers, A. and Hansen, C. (1997). Experimental Psychology. Brooks/Cole Publishing Company.
【19】Duda, R. O., Hart, P. E., and Stork, D. G... (2001). Pattern Classification, Second Edition. John Wiley & Sons, Inc.
【20】Liao, W. H., Chang, C.C. (2004). Embedding Information within Dynamic Visual Patterns. The 2004 IEEE International Conference On Multimedia And Expo.
【21】王文中,(1999),「統計學與Excel資料分析之實習應用」,博碩文化。
【22】李江山、孫慶文、陳一平、陳建中、黃淑麗、黃榮村、葉素玲、襲充文、櫻井正二郎,(2002),「視覺與認知–視覺知覺與視覺運動系統」,遠流。
【23】洪蘭、曾志朗譯,(1997),「心理學實驗研究法」,遠流。
描述 碩士
國立政治大學
資訊科學學系
91753014
93
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0917530141
資料類型 thesis
dc.contributor.advisor 廖文宏zh_TW
dc.contributor.advisor Wen-Hung Liaoen_US
dc.contributor.author (作者) 張繼志zh_TW
dc.contributor.author (作者) Chi-Chih Changen_US
dc.creator (作者) 張繼志zh_TW
dc.creator (作者) Chi-Chih Changen_US
dc.date (日期) 2004en_US
dc.date.accessioned 17-九月-2009 14:06:36 (UTC+8)-
dc.date.available 17-九月-2009 14:06:36 (UTC+8)-
dc.date.issued (上傳時間) 17-九月-2009 14:06:36 (UTC+8)-
dc.identifier (其他 識別碼) G0917530141en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/32710-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 91753014zh_TW
dc.description (描述) 93zh_TW
dc.description.abstract (摘要) 隨著科技的進步與資訊科學的發展,大量的資訊處理自動化逐漸取代傳統人工技術,然而不恰當地使用自動化技術,卻可能危害人類的權益與空間。為避免過度濫用機器自動化對人類所造成的災害,本研究根據不同的適用情境,分別提出以靜態及動態圖型為基礎的人機區分方法,透過簡單的影像處理技術,產生機器難以分析但人類能夠易於判別的人機辨識影像。並且由認知的角度,設計實驗進一步探討人類視覺優勢以及接受度,作為影像產生時的標準。最後,提出人機區分技術與應用情境整合實作的方法,以觀實效。zh_TW
dc.description.abstract (摘要) The idea of using a computer program to distinguish humans from machines, sometimes referred to as the “Reverse Turing Test”, has emerged only quite recently. The term CAPTCHA, which stands for “Completely Automated Public Turing Test to Tell Computers and Humans Apart", is defined as:
“a program that can generate and grade tests that:
□ Most human can pass
but
□ Current computer program can’t pass! “
In this thesis, a texture-image based approach is developed to encode text information in such a way that machine vision algorithms will experience significant difficulties while human can extract the embedded text effortlessly. Both static images and dynamic sequences will be explored. It is anticipated that the cost of storing, and subsequently decoding information from such visual patterns will be prohibitedly high, both in terms of time and space complexity. To validate the postulation, fundamental principles of the human cognitive process will be examined. Experiments will also be carried out to gather user feedback and investigate the limitations of human visual systems. Finally, several application scenarios that call for the integration of a CAPTCHA will be identified and discussed.
en_US
dc.description.tableofcontents 第一章 緒論.........................................................................................................1
1.1 研究背景與目的..................................................................................1
1.2 CAPTCHA...........................................................................................2
1.3 人類視覺優勢......................................................................................9
第二章 靜態單一影像.......................................................................................13
2.1 原理....................................................................................................13
2.2 過程....................................................................................................14
2.3 結果....................................................................................................14
第三章 動態隨機點材質影像...........................................................................21
3.1 原理....................................................................................................21
3.2 過程....................................................................................................21
3.3 結果....................................................................................................23
第四章 人類視覺系統接受度...........................................................................27
4.1 實驗一................................................................................................28
4.1.1 實驗目的...................................................................................28
4.1.2 實驗方法...................................................................................28
4.1.3 實驗結果與討論.......................................................................31
4.2 實驗二................................................................................................47
4.2.1 實驗目的...................................................................................47
4.2.2 實驗方法...................................................................................47
4.2.3 實驗結果與討論.......................................................................48
4.3 實驗三................................................................................................63
4.3.1 實驗目的...................................................................................63
4.3.2 實驗方法...................................................................................63
4.3.3 實驗結果與討論.......................................................................66
4.4 總體實驗結果與討論........................................................................83
第五章 導入實際應用............................................................................85
5.1 靜態單一影像....................................................................................85
5.1.1 大學選課系統...........................................................................85
5.1.2 網路售票系統...........................................................................85
5.1.3 投票系統...................................................................................85
5.1.4 網路投標、兢標系統.................................................................86
5.2 動態隨機點材質影像........................................................................87
5.2.1 線上遊戲...................................................................................87
第六章 結論.......................................................................................................89
參考文獻.............................................................................................................91
附錄.....................................................................................................................94
zh_TW
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dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0917530141en_US
dc.subject (關鍵詞) 人機辨識zh_TW
dc.subject (關鍵詞) 質感圖樣zh_TW
dc.subject (關鍵詞) CAPTCHAen_US
dc.subject (關鍵詞) Turning Testen_US
dc.subject (關鍵詞) Textureen_US
dc.subject (關鍵詞) Visual Patternen_US
dc.title (題名) 植基於質感圖樣之自動化人機區分機制zh_TW
dc.title (題名) A CAPTCHA Mechanism Based on Textured Patternsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 【1】Berners-Lee, T., Hendler, J. and Lassila, O. (2001). The Semantic Web. Scientific American.zh_TW
dc.relation.reference (參考文獻) 【2】Turing, A. (1950). Computing machinery and intelligence, artificial intelligence.zh_TW
dc.relation.reference (參考文獻) 【3】Ahn, L von., Blum, M., Hopper, N. J., and Langford, J. (2003). CAPTCHA: Telling Humans and Computers Apart (Automatically). Advances in Cryptology, Eurocrypt `03, volume 2656 of Lecture Notes in Computer Science, 294–311.zh_TW
dc.relation.reference (參考文獻) 【4】Mori, G. and Malik, J. (2003). Recognizing objects in adversarial clutter: Breaking a visual CAPTCHA. In Proceedings of the Conference on Computer Vision and Pattern Recognition,. Vol. I, pp.134-141, Madison, USA.zh_TW
dc.relation.reference (參考文獻) 【5】Kochanski, G., Lopresti, D., and Shih, C. (2002). A Reverse Turing Test Using Speech. Seventh International Conference on Spoken Language Processing, 16-20.zh_TW
dc.relation.reference (參考文獻) 【6】Julesz, B. and Miller, J.E. (1962). Automatic stereoscopic presentation of functions of two variables. Bell System Technical Journal, 41: 663-676.zh_TW
dc.relation.reference (參考文獻) 【7】Goldstein, E. B. (1999). Sensation and Perception, Fifth Edition, Brooks/Cole Publishing Company.zh_TW
dc.relation.reference (參考文獻) 【8】Kanizsa, G.. (1955). Margini quasi-percettivi in campi con stimolazione omogenea. Rivista di psicologia, 49, 7-30.zh_TW
dc.relation.reference (參考文獻) 【9】Bradley, D. R., and Petry, H. M. (1977). Organizational determinants of subjective contour: The subjective Necker cube. American Journal of Psychology, 90, 253-262.zh_TW
dc.relation.reference (參考文獻) 【10】Williams, L. R. and Jacobs, D. W. (1997). Stochastic Completion Fields: A Neural odel of Illusory Contour Shape and Salience, Neural Computation, Vol. 9, No. 4, pp. 837-858.zh_TW
dc.relation.reference (參考文獻) 【11】Julesz, B. (1962). Visual pattern discrimination. IRE Trans Inf Theory, IT-8:84-92.zh_TW
dc.relation.reference (參考文獻) 【12】Julesz, B. (1975). Experiments on the visual perception of texture. Scientific American, 232, 34-43.zh_TW
dc.relation.reference (參考文獻) 【13】Julesz, B. Textons. (1981). the elements of texture perception and their interactions. Nature, London, 290, 91-97.zh_TW
dc.relation.reference (參考文獻) 【14】Regan, D. (1986). Luminance contrast: Vernier discrimination. Spatial Vision, 1, 305-318.zh_TW
dc.relation.reference (參考文獻) 【15】Newsome, W. T., Britten, K. H., and Movshon, J. A. (1989). Neuronal correlates of a perceptual decision. Nature, 341, 52-54.zh_TW
dc.relation.reference (參考文獻) 【16】Gonzalez, R. C. and Woods, R. E. (2001). Digital Image Processing Second Edition. Prentice-Hall, Inc.zh_TW
dc.relation.reference (參考文獻) 【17】Forsyth, D. A.,and Ponce, J. (2003). Computer Vision: A Modern Approach. Prentice-Hall, Inc.zh_TW
dc.relation.reference (參考文獻) 【18】Myers, A. and Hansen, C. (1997). Experimental Psychology. Brooks/Cole Publishing Company.zh_TW
dc.relation.reference (參考文獻) 【19】Duda, R. O., Hart, P. E., and Stork, D. G... (2001). Pattern Classification, Second Edition. John Wiley & Sons, Inc.zh_TW
dc.relation.reference (參考文獻) 【20】Liao, W. H., Chang, C.C. (2004). Embedding Information within Dynamic Visual Patterns. The 2004 IEEE International Conference On Multimedia And Expo.zh_TW
dc.relation.reference (參考文獻) 【21】王文中,(1999),「統計學與Excel資料分析之實習應用」,博碩文化。zh_TW
dc.relation.reference (參考文獻) 【22】李江山、孫慶文、陳一平、陳建中、黃淑麗、黃榮村、葉素玲、襲充文、櫻井正二郎,(2002),「視覺與認知–視覺知覺與視覺運動系統」,遠流。zh_TW
dc.relation.reference (參考文獻) 【23】洪蘭、曾志朗譯,(1997),「心理學實驗研究法」,遠流。zh_TW