摘要
驗證碼（Completely Automated Public Turing test to tell Computers and Humans Apart, CAPTCHA）是預防駭客利用自動機器人程式濫用網路服務資源的安全性措施，其中以文字類型驗證碼最為普遍，但文字類型驗證碼仍然可透過電腦程式辨識而被破解。因此，文字型驗證碼需加上干擾的設計使電腦不易辨識，但過度的干擾設計可能會增加使用者在辨識時視覺與認知的負荷。
由於現有OCR軟體可利用字典的功能來加以辨識其文字驗證碼，因此目前文字驗證碼的設計普遍為word（文字組合的單字）與non-word（無意義的字母組合）兩種來提供使用者做辨識。在認知的過程中發現我們對於word較為熟悉的字屬於由上而下的處理過程反應速度較為快速，相較於non-word視覺系統必須將這些刺激分析並組合成一個字元是屬於由下而上的處理過程則會相對較慢，基於上述的因素將word與non-word兩組為第一個實驗因子。先前研究比較過文字驗證碼的反應時間，發現其中較難辨識的文字驗證碼和過去occluded object recognition研究中所提出的理論有所關聯，其中遮蔽文字的前景物件為visible（看得見的）是屬於線條干擾，前景物件呈現反白為invisible（看不見的）屬於切割干擾，不是物件也不是反白則為incoherent（不一致的）屬於雜點干擾，以上三種干擾組合為第二個實驗因子。本研究為探討這些因子組合的認知過程，以眼球追蹤儀觀察受測者眼球追蹤資料，比較受測者在辨識不同組合驗證碼時，工作績效、視覺疲勞、工作負荷及認知過程的差異。
研究結果顯示種類incoherent顯著縮短反應的時間且錯誤率較低。此外，用字word組合有助於提高受測者的辨識績效，並能有效減少主觀視覺上的疲勞。invisible干擾設計則會影響每個字元的眼球追蹤資料，顯示此干擾容易增加視覺上的負荷。文字閱讀認知過程由下而上的處理方式不但會增加使用者辨識上的負荷，甚至造成使用者的困擾。建議往後文字驗證碼設計者可搭配種類incoherent與用字non-word組合，以及種類visible與用字word組合，除了能有效防止文字驗證碼遭受破解外，並能減少辨識時認知處理上的負荷，以提高使用者通過的機率。

Abstract
CAPTCHA, the acronym of “Completely Automatic Public Turning test to tell Computers and Human Apart”, is kind of security measures for preventing hacker to overuse the internet service and resource. However, text-based CAPTCHA is the most common security measure but this way also could be deciphered by computer program. Therefore, text-based CAPTCHA must add the disturbance on the design to cause the computer program could not easy to recognize, but the excessive disturbance design will possibly increase the identification visual and cognition loading when user is trying to recognize something.
Currently, OCR software can tell text-based CAPTCHA by using dictionary function so that the general designs of text-based CAPTCHA are word and non-word for users’ identification. In the course of cognition, we find that the familiar words are in a process from top-down with rapid reaction speed. However, compared to non-word visual system, it must form these stimulated analyses into a character in the process from bottom-up and the speed is slow. Word and non-word are treated as the first experimental factor based on the above reasons. The previous research has compared with the reactive time of text-based CAPTCHA and finds the difficult text-based CAPTCHA is related with the theory proposed in the occluded object recognition research. The foreground objects of covered characters are visible and belong to Thread Noise. If the foreground object is the reverse type, it is invisible and belongs to Line Mask. If foreground object is neither the object nor reverse type, it is incoherent and belongs to Blot Mask. The above three kinds of interferences are treated as the second experimental factor. This research discusses the cognitive process of these factor groups and observes subjects’ eye-tracking data through the eye tracker as well as compares subjects’ difference among working performance, visual fatigue, workload and cognitive process in different groups’ text-based CAPTCHA.
The research results show incoherence cuts response time obviously and the error rate is lower. Moreover, word combination helps to improve the identification performance of the subject and is effective in reducing the fatigue in visual sense. However, invisible interference design can affect the eye-tracking data of each character, easily increasing the load of visual sense. The bottom-up processing in text reading and cognition will not only increase the load of users in identification, but even make trouble for users. We suggest that designers of text-based CAPTCHA collocate incoherence with non-word combination, and collocate visibility with word combination. It is effective in both preventing text-based CAPTCHA being cracked and reducing the load of identification in cognition processing so as to enhance users’ passing rate.

目錄
摘要 I
Abstract III
致謝 V
目錄 VI
表目錄 XI
圖目錄 XIII
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 3
1.3 研究範圍與限制 4
1.4 研究流程 4
第二章 文獻探討 7
2.1 驗證碼 7
2.1.1 驗證碼類型 7
2.1.2 文字驗證碼干擾設計 10
2.2 眼球運動 13
2.2.1 閱讀文字的眼球運動 14
2.2.2 圖像場景的眼球運動 15
2.2.3 圖片與文字的眼球運動 17
2.3 文字閱讀認知過程 19
2.4 質化方法分析眼球運動 20
2.5 眼球追蹤資料分析指標 22
第三章 研究方法與設計 24
3.1 研究架構 24
3.2 受測者 24
3.3 實驗設備與環境配置 25
3.3.1 眼球追蹤儀 25
3.3.2 MangoldVision Analyzer眼動資料軟體 26
3.3.3 實驗環境配置 26
3.4 實驗設計 28
3.4.1 自變項 28
3.4.2 依變項 31
3.4.3 控制變項 35
3.5 實驗程序與步驟 36
3.5.1 實驗程序 36
3.5.2 實驗步驟 38
3.6 資料分析方法 42
第四章 研究結果與分析 43
4.1 受測者基本資料 43
4.2 反應時間 44
4.3 錯誤個數 45
4.4視覺疲勞主觀差異量 46
4.5 NASA-TLX心智負荷量 47
4.6 眼球追蹤資料 48
4.6.1熱區圖與軌跡圖 48
4.6.2 各AOI眼球追蹤資料 51
4.6.3 AOI平均凝視時間 55
4.6.4 AOI平均凝視點次數 56
4.6.5 AOI平均回視次數 57
4.6.6 眼球追蹤資料分群 58
第五章 研究討論 62
5.1 反應時間之探討 62
5.2 錯誤個數之探討 63
5.3 視覺疲勞主觀差異量之探討 63
5.4 NASA-TLX心智負荷量之探討 64
5.5 眼球追蹤資料之探討 65
第六章 結論與建議 69
6.1 結論 69
6.2 未來研究方向 71
參考文獻 72
中文部份 72
英文部份 73
附錄 A 80
受測者須知及實驗同意書 80
附錄 B 81
受測者基本資料問卷調查 81
附錄 C 82
視覺疲勞主觀評量表 82
附錄 D 83
NASA-TLX主觀心智評量表 83
附錄 E 84
Word字母出現頻率 84
附錄 F 85
Non-word字母出現頻率 85
附錄 G 86
受測者組別順序 86
附錄 H 87
正式實驗圖片出現順序 87
附錄 I 88
受測者填寫英文單字卡 88



























表目錄
表3-1 驗證碼錯誤個數範例 31
表3-2 每一組別之實驗時間與休息時間 37
表3-3 種類與用字組合對依變項之常態檢定 42
表4-1 受測者基本資料 43
表4-2 種類與用字對反應時間之敘述性統計 44
表4-3 種類與用字對反應時間之無母數檢定 44
表4-4 種類與用字對錯誤個數之敘述性統計 45
表4-5 種類與用字對錯誤個數之無母數檢定 45
表4-6 種類與用字對視覺疲勞主觀差異量之敘述性統計 46
表4-7 種類與用字對視覺疲勞主觀差異量之無母數檢定 46
表4-8 種類與用字對NASA-TLX心智負荷量之敘述性統計 47
表4-9 種類與用字對NASA-TLX心智負荷量之無母數檢定 47
表4-10 種類與用字組合各AOI平均凝視時間 52
表4-11 種類與用字組合各AOI平均凝視點次數 53
表4-12 種類與用字組合各AOI平均回視次數 54
表4-13 種類與用字對AOI平均凝視時間之敘述性統計 55
表4-14 種類與用字對AOI平均凝視時間之無母數檢定 55
表4-15 種類與用字對AOI平均凝視點次數之敘述性統計 56
表4-16 種類與用字對AOI平均凝視點次數之無母數檢定 56
表4-17 種類與用字對AOI平均回視次數之敘述性統計 57
表4-18 種類與用字對AOI平均回視次數之無母數檢定 57
表4-19 種類與用字組合VXB分群績效指標值比較表 59
表4-20 種類與用字組合分群歸屬程度比較表 59














圖目錄
圖1-1 reCAPTCHA 2
圖1-2 利用OCR辨識分析字元 2
圖1-3 研究流程圖 6
圖2-1 字體干擾文字驗證碼 10
圖2-2 字體干擾文字驗證碼 10
圖2-3 背景干擾文字驗證碼 11
圖2-4 遮蔽干擾文字驗證碼 11
圖2-5 切割干擾文字驗證碼 11
圖2-6 雜點數量干擾文字驗證碼 12
圖2-7 雜訊干擾文字驗證碼 12
圖2-8 遮蔽與切割干擾 12
圖2-9 場景圖像眼球移動軌跡 16
圖2-10 人臉圖像眼球移動軌跡 16
圖2-11掃視文字與圖片的模式 18
圖2-12 藝術圖畫場景 21
圖2-13 熱區圖 21
圖2-14 3D熱區圖 21
圖2-15 注視圖 21
圖3-1 MangoldVision眼球追蹤儀 25
圖3-2 MangoldVision眼動資料軟體介面 26
圖3-3 實驗環境佈置 27
圖3-4 觀察人員監測受測者畫面 28
圖3-5 受測者距離螢幕距離 28
圖3-6 四種干擾組合 29
圖3-7 實驗之文字驗證碼 30
圖3-8 實驗說明階段流程圖 36
圖3-10 5點校正畫面 40
圖3-11 校正品質畫面 40
圖4-4 種類與用字組合之熱區圖 49
圖4-5 種類與用字組合之軌跡圖 50
圖4-6 種類與用字組合之影像區域編輯 51
圖4-7 種類與用字組合各AOI平均凝視時間之折線圖 52
圖4-8 種類與用字組合各AOI平均凝視點次數之折線圖 53
圖4-9 種類與用字組合各AOI平均回視次數之折線圖 54
圖4-12 Fuzzy C-Means分群語法 58
圖4-13 種類與用字組合之分群圖 61

中文部份
伊彬、林演慶，(2006)，視覺影像處理之眼球運動相關研究探討，設計學報第11卷第4期。
日本厚生労働省労働基準局(2002)，新しい「ＶＤＴ作業における労働衛生管理のためのガイドライン」の策定について。
取自: http://www.mhlw.go.jp/houdou/2002/04/h0405-4.html
徐志翔，(2010)，探討年齡對文字驗證碼工作之影響，碩士論文，朝陽科技大學工業工程與管理系。
陳玥君，(2011)，應用AR教具於單雙子葉植物特徵學習之眼動評估，碩士論文，台南大學數位學習科技學系。
張育銘，(2007)，購物網站介面設計樣式對消費者使用性影響之研究，碩士論文，國立成功大學工業設計系。
蔡介立、顏妙琁、汪勁安，(2005)，眼球移動測量及在中文閱讀研究之應用，應用心理研究，第28期，91-104頁。
陳學志、賴惠德、邱發忠，(2010)，眼球追蹤技術在學習與教育上的應用，眼球追蹤與學習教育，第55期，第4卷，39-68頁。
陳弘庭，(2001)，模糊分群方法、語意變數、分群群數關係之研究─以市場區隔為例，碩士論文，國立成功大學 工業管理科學系。
英文部份
Ahn, L., Blum, M., & Hopper, N. J. (2004). Telling humans and computers apart (Automatically) or How lazy cryptographers do AI. Communications of the ACM, 47, 57–60.
Broadbent, D. E., & Broadbent, M. H. (1980). Priming and the passive/active model of word recognition, In R. Nikerson (Eds.), Attention and performance VIII. New York: Academic Press.
Buswell, G. T. (1935). How people look at pictures. Chicago: University of Chicago Press.
Chellapilla, K., Larson, K., Simard, P. Y., & Czerwinski, M. (2005). Building segmentation based human-friendly human interaction proofs (HIPs). Proceedings of Second International Workshop on Human Interactive Proofs, May 2005, 1–26.
Chew, M., & Baird, H. S. (2003). Baffle Text: a Human Interactive Proof, 10th IS&T/SPIE Document Recognition, 305–316.
De Graef, P., Christiaens, D., & d''Ydewalle, G. (1990). Perceptual effects of scene context on object identification. Psychological Research, 52(4), 317–329.
Duchowski, A. T., (2002). A breadth-first survey of eye-tracking applications. Behavior Research Methods, 34(4), 455–470.
Fitts, P. M., Jones, R. E., & Milton, J. L. (1950). Eye movements of aircraft pilots during instrument-landing approaches. Aeronautical Engineering Review 9(2), 24–29.
Friedman, A. (1979). Framing pictures: The role of knowledge in automatized encoding and memory for gist. Journal of Experimental Psychology: General, 108(3), 316–355.
Haber, R. N., & Schindler, R. M. (1981). Error in Proofreading: Evidence of Syntactic Control of Letter Processing. Journal of Experimental Psychology: Human Perception and Performance, 7(3), 573–579.
Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Human mental workload, Amsterdam: North Holland Press, 239–250.
Henderson, J. M., & Hollingworth, A. (1999). High-level scene perception. Annual Review of Psychology, 50(1), 243–271.
Henderson, J. M., Weeks Jr, P. A., & Hollingworth, A. (1999). The effects of semantic consistency on eye movements during complex scene viewing. Journal of Experimental Psychology: Human Perception and Performance, 25(1), 210–228.
Heuer, H., Hollendiek, G., Kröger, H., & Römer, T. (1989). Die Ruhelage der Augen und ihr Einfluss auf Beobachtungsabstand und visuelle Ermüdung bei Bildschirmarbeit. Zeitschrift für experimentelle und angewandte psychologie, 36(4), 538–566.
Hoffman, J. E., & Subramaniam, B. (1995). The role of visual attention in saccadic eye movements. Attention, Perception, & Psychophysics, 57(6), 787–795.
Hughes, A., Wilkens, T., Wildemuth, B., & Marchionini, G. (2003). Text or pictures? An eyetracking study of how people view digital video surrogates. Proceedings of the International Conference on Image and Video Retrieval (CIVR 2003), 271–280.
Hyona, J., Lorch Jr, R. F., & Kaakinen, J. K. (2002). Individual differences in reading to summarize expository text: Evidence from eye fixation patterns. Journal of Educational Psychology, 94(1), 44–55.
Jacob, R. J. (1990). What you look at is what you get: eye movement-based interaction techniques. Proceedings of the SIGCHI conference on Human factors in computing systems: Empowering people, 11–18.
Jacob, R. J. K., & Karn, K. S. (2003). Eye tracking in Human-Computer Interaction and usability research: Ready to deliver the promises, In J. Hyönä, R. Radach, & H. Deubel (Eds.), The mind''s eye: Cognitive and applied aspects of eye movement research. Amsterdam: Elsevier, 573-605.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264–323.
Just, M. A., & Carpenter, P. A. (1980). A theory of reading: from eye fixations to comprehension. Psychological Review, 87(4), 329–354.
Kolupaev, A., & Ogijenko, J. (2008). Captchas: Humans vs. bots. Security & Privacy, IEEE, 6(1), 68–70.
Kotval, X. P., & Goldberg, J. H. (1998). Eye movements and interface components grouping: An evaluation method. Proceedings of the 42nd Annual Meeting of the Human Factors and Ergonomics Society, 486–490.
Lee, Y. L., & Hsu, C. H. (2011). Usability study of text-based CAPTCHAs. Displays, 32(2), 81–86.
Loftus, G. R., & Mackworth, N. H. (1978). Cognitive determinants of fixation location during picture viewing. Journal of Experimental Psychology: Human Perception and Performance, 4(4), 565–572.
Mackworth, N. H., & Morandi, A. J. (1967). The gaze selects informative details within pictures. Attention, Perception, & Psychophysics, 2(11), 547–552.
McClelland, J. L., & Johnston, J. C. (1977). The role of familiar units in perception of words and nonwords. Attention, Perception, & Psychophysics, 22(3), 249–261.
McConkie, G. W., & Rayner, K. (1975). The span of the effective stimulus during a fixation in reading. Perception & Psychophysics, 17(6), 578–586.
Mori, G., & Malik, J. (2003). Recognizing Objects in Adversarial Clutter: Breaking a Visual CAPTCHA. Computer Vision and Pattern Recognition, IEEE Computer Society Conference, 1, 134–141.
Nakayama, K., Shimojo, S., & Silverman, G. H. (1989). Stereoscopic depth: its relation to image segmentation, grouping, and the recognition of occluded objects. Perception, 18(1), 55–68.
Pal, N., & Bezdek, J. (1995). On cluster validity for the fuzzy c-means model. Fuzzy Systems, IEEE Transactions on, 3(3), 370-379.
Pal, N., & Bezdek, J. (1997). Correction to "On Cluster Validity for the Fuzzy c-Means Model" [Correspondence]. Fuzzy Systems, IEEE Transactions on, 5(1), 152-153.
Pan, B., Hembrooke, H. A., Gay, G. K., Granka, L. A., Feusner, M. K., & Newman, J. K. (2004). The determinants of web page viewing behavior: an eye-tracking study. Proceedings of the 2004 symposium on Eye tracking research & applications, 147–154.
Pelz, J. B., Canosa, R., & Babcock, J. (2000). Extended tasks elicit complex eye movement patterns. Proceedings of the 2000 symposium on Eye tracking research & applications, 37–43.
Pernice, K., & Nielsen, J. (2009). Eyetracking methodology: How to conduct and evaluate usability studies using eyetracking. USA: Nielsen Norman Group, 1–163.
Rayner, K. (1992). Eye movements and visual cognition: Scene perception and reading. New York: Springer-Verlag, 428–448.
Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological bulletin, 124(3), 372–422.
Rayner, K., & Pollatsek, A. (1989). The psychology of reading. Prentice-Hall Erlbaum Publishers.
Rayner, K., Rotello, C. M., Stewart, A. J., Keir, J., & Duffy, S. A. (2001). Integrating text and pictorial information: Eye movements when looking at print advertisements. Journal of Experimental Psychology: Applied, 7(3), 219-226.
Shirali-Shahreza, M., & Shirali-Shahreza, S. (2008). CAPTCHA systems for disabled people. Intelligent Computer Communication and Processing, 2008. 4th International Conference, 319–322.
Von Ahn, L., Blum, M., Hopper, N., & Langford, J. (2003). CAPTCHA: Using hard AI problems for security. Advances in Cryptology—EUROCRYPT 2003, 294–311.
Von Ahn, L., Maurer, B., McMillen, C., Abraham, D., & Blum, M. (2008). recaptcha: Human-based character recognition via web security measures. Science, 321, 1465–1468.
Wooding, D. S. (2002). Fixation maps: quantifying eye-movement traces. Proceedings of the 2002 symposium on Eye tracking research & applications, 31–36.
Yan, J., & El Ahmad, A. S. (2008). Usability of CAPTCHAs or usability issues in CAPTCHA design. Proceedings of the 4th symposium on Usable privacy and security, 44–52.
Yarbus, A. L. (1967). Eye movements during perception of complex objects. Eye movements and vision, 7, 171–196.