Table 1 Numbers of images in cases of which numbers of input stringsis4and Microsoft CAPTCHA CAPTCHA [6] Fig. 1
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- みちしげ すえたけ
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1 CAPTCHA ,a) , Web CAPTCHA Web CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA A Proposal of an Image-based CAPTCHA Using Random Obstruction Figures to Absorb OCR-based Bot-attacks Takumi Tamura 1 Shin-ichiro Kubota 1 Kentaro Aburada 2 Tetsuro Katayama 1 Mirang Park 3 Naonobu Okazaki 1,a) Received: June 25, 2014, Accepted: December 3, 2014 Abstract: A reversal turing test called CAPTCHA is used in many webservice sites to prevent the automatic program called bots from making unauthorized accounts. The CAPTCHA with images of correct answer string, called as the text-based CAPTCHA, is widely prevalent because of an ease implementing in the website. The optical character recognition technologies enable bots solve the text-based CAPTCHA automatically. Any researchers have pointed out the vulnerability of the text-based CAPTCHA. Absorbing the vulnerability of the text-based CAPTCHA, the image-based CAPTCHA is proposed, which use the human abilities to discern objects in images. However, the existing image-based CAPTCHAs also have problems about usability and robustness. In this paper, we propose a new image-based CAPTCHA using images without a correct answer string and with obstruction figures, to achieve high usability and robustness. In order to confirm a effectiveness of the proposed method, we argue to absorb some considerable attacks in the image-based CAPTCHA, and conduct assessment of usability through our questionnaire. The results show that the proposed system absorb the attacks adequately and has usability. Keywords: image-based CAPTCHA, bot, obstruction figures 1 University of Miyazaki, Miyazaki , Japan 2 Oita National College of Technology, Oita , Japan 3 Kanagawa Institute of Technology, Atsugi, Kanagawa , Japan a) oka@cs.miyazaki-u.ac.jp 1. Web Web Web CMU Carnegie Melc 2015 Information Processing Society of Japan 808
2 Table 1 Numbers of images in cases of which numbers of input stringsis4and Microsoft CAPTCHA CAPTCHA [6] Fig. 1 CAPTCHA [6] used on Microsoft Web site. lon University CAPTCHA Completely Automated Public Turing test to tell Computers and Humans Apart [1] CAPTCHA / Web 1 CAPTCHA CAPTCHA OCR Optical Character Recognition OCR [2], [3] OCR CAPTCHA [4] CAPTCHA [5] CAPTCHA 2. CAPTCHA CAPTCHA 2.1 CAPTCHA CAPTCHA CAPTCHA CAPTCHA Gimpy [7] EZ-Gimpy [7] r-gimpy [8] recaptcha [9] Gimpy CAPTCHA CAPTCHA EZ-Gimpy r-gimpy Gimpy 1 CAPTCHA recaptcha OCR CAPTCHA CAPTCHA Web a CAPTCHA 62 a 4 10 CAPTCHA 1 CAPTCHA OCR Mori [7] OCR 191 EZ-Gimpy 83% CAPTCHA OCR CAPTCHA 2.2 CAPTCHA CAPTCHA CAPTCHA Asirra [4] 4 CAPTCHA [10] What s Up CAPTCHA [11] Cortcha Challenge [12] MULTI-MODAL CAPTCHA [13] Image- Text Fusion CAPTCHA [14] [15] [16] Use Your Illusion [17] 1 c 2015 Information Processing Society of Japan 809
![CAPTCHA [5] CAPTCHA 2. CAPTCHA CAPTCHA 2.](/docs-images/42/22825307/images/page_2.jpg "1 CAPTCHA CAPTCHA CAPTCHA CAPTCHA Gimpy [7] EZ-Gimpy [7] r-gimpy [8] recaptcha [9] Gimpy 2 1 5 10 3 CAPTCHA CAPTCHA EZ-Gimpy r-gimpy Gimpy 1 CAPTCHA recaptcha OCR CAPTCHA CAPTCHA Web 52 10 62 a")
3 CAPTCHA Asirra 12 4 CAPTCHA 4 4 What s Up CAPTCHA Cortcha Challenge CAPTCHA CAPTCHA CAPTCHA MULTI-MODAL CAPTCHA Image-Text Fusion CAPTCHA CAPTCHA CAPTCHA CAPTCHA OCR CAPTCHA OCR CAPTCHA CAPTCHA CAPTCHA 1 CAPTCHA 12 a (1) 1 12C a (1) a a 1/4,095 *1 * a=1 12C a = 1 4, CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA Web CAPTCHA SVM CAPTCHA CAPTCHA [5] SVM 10.3% Asirra CAPTCHA CAPTCHA OCR CAPTCHA CAPTCHA CAPTCHA CAPTCHA NuCAPTCHA [18] CAPTCHA [19] CAPTCHA [20] CAPTCHA NuCAPTCHA c 2015 Information Processing Society of Japan 810
![1 4 CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA Web CAPTCHA SVM CAPTCHA CAPTCHA [5] SVM 10.](/docs-images/42/22825307/images/page_3.jpg "3% Asirra CAPTCHA CAPTCHA OCR CAPTCHA CAPTCHA 1 2.")
4 CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA 3. CAPTCHA 2 CAPTCHA OCR CAPTCHA OCR CAPTCHA CAPTCHA 2 CAPTCHA CAPTCHA CAPTCHA CAPTCHA 1 CAPTCHA 1 OCR CAPTCHA [16], [17] CAPTCHA [11] CAPTCHA 1 2 CAPTCHA CAPTCHA CAPTCHA OCR CAPTCHA IC-CAPTCHA Imaged-based Character input type CAPTCHA IC-CAPTCHA 3.1 IC-CAPTCHA IC-CAPTCHA 2 CAPTCHA Table 2 Problems of CAPTCHA. OCR OCR OCR CAPTCHA c 2015 Information Processing Society of Japan 811
![[17] CAPTCHA [11] CAPTCHA 1 2 CAPTCHA CAPTCHA CAPTCHA OCR CAPTCHA IC-CAPTCHA Imaged-based Character input type](/docs-images/42/22825307/images/page_4.jpg "CAPTCHA IC-CAPTCHA 3.1 IC-CAPTCHA IC-CAPTCHA 2 CAPTCHA Table 2 Problems of CAPTCHA.")
5 2 IC-CAPTCHA Fig. 2 A flowchart of the IC-CAPTCHA. IC-CAPTCHA 2 IC-CAPTCHA Step1 IC-CAPTCHA 1 Step2 Step1 1 Step3 Step2 Step4 Step3 Step3 Step5 Step3 Step6 Step3 Step7 Step4 5 6 Step1 Step8 Step9 Step10 IC-CAPTCHA k Step9 k +1 Step (1) (2) WordNet [21] 3.3 C++ Open CV PC Ubuntu11.10 IC-CAPTCHA CAPTCHA IC-CAPTCHA CAPTCHA Step1 Step3 Step8 Step9 Step10 Step2 Web c 2015 Information Processing Society of Japan 812
6 3 Fig. 3 An example of an IC-CAPTCHA image (apple). Google Step3 srandom IC-CAPTCHA 3 4. Web CAPTCHA OCR IC-CAPTCHA OCR CAPTCHA OCR 2 brute-force IC- CAPTCHA brute-force brute-force Step1 brute-force CAPTCHA [4] 4,096 [22] 16,000 brute-force IC-CAPTCHA Step IC-CAPTCHA bruteforce CAPTCHA 1/4,096 4,096 WordNet 93,834 WordNet 3.2 4,096 4,096 4,096 CAPTCHA CAPTCHA CAPTCHA [23] WordNet Step6 4 Step5 IC-CAPTCHA c 2015 Information Processing Society of Japan 813
![[22] 16,000 brute-force IC-CAPTCHA Step10 1 3 IC-CAPTCHA bruteforce CAPTCHA 1/4,096 4,096 WordNet 93,834 WordNet 3.](/docs-images/42/22825307/images/page_6.jpg "2 4,096 4,096 4,096 CAPTCHA 2 2 2 CAPTCHA CAPTCHA 64 100 [23] WordNet 64 2 3 1 Step6 4 Step5 IC-CAPTCHA c 2015 Information")
7 4.3 5 CAPTCHA Asirra SVM 2 CAPTCHA Asirra 6 IC-CAPTCHA Web IC-CAPTCHA SD = IC-CAPTCHA Google [24] (a) 10 3 Web (b) 3 10 (c) Google 1 4 Google Google Google Google (b) 5 (b) Step5 3 Table 3 Results of the survey about a number of the obstruct figures Table 4 4 Numbers of images for the survey about image search evaluation c 2015 Information Processing Society of Japan 814
8 Table 5 5 Results of image search evaluation. 6 Table 6 The usability evaluation items % [25] CAPTCHA IC-CAPTCHA CAPTCHA CAPTCHA CAPTCHA CAPTCHA Gimpy-r [6] CAPTCHA Asirra [4] Gimpy-r Asirra CAPTCHA Gimpy-r Asirra IC-CAPTCHA Gimpy-r Asirra IC-CAPTCHA 10 Gimpy-r Windows 7 Windows Asirra Windows 7 Asirra Web IC-CAPTCHA VMware Ubuntu11.10 CAPTCHA CAPTCHA Gimpy-r IC-CAPTCHA Asirra CAPTCHA CAPTCHA 1 PC CAPTCHA 1 5 Web Table 7 The usability evaluation results. IC-CAPTCHA Asirra CAPTCHA CAPTCHA CAPTCHA Web Table 8 Required times and correct answer rates. % sec IC-CAPTCHA CAPTCHA Asirra IC- CAPTCHA CAPTCHA CAPTCHA 8 IC-CAPTCHA [28] 5 IC-CAPTCHA 11.3% CAPTCHA CAPTCHA Asirra IC-CAPTCHA CAPTCHA IC-CAPTCHA CAPTCHA c 2015 Information Processing Society of Japan 815
9 orange lemon PC computer Web [26] Web 5. Web CAPTCHA CAPTCHA CAPTCHA IC-CAPTCHA CAPTCHA OCR CAPTCHA CAPTCHA CAPCTHA CAPTCHA CAPTCHA 1 CAPTCHA CAPTCHA Web CAPTCHA IC-CAPTCHA CAPTCHA IC-CAPTCHA CAPTCHA Web [27] [1] Von Ahn, L., Blum, M., Hopper, N.J. and Langford, J.: CAPTCHA: Telling humans and computers apart, Advances in Cryptology, Eurocrypt 03, Vol.2656 of Lecture Notes in Computer Science, pp (2003). [2] Yan, J. and El Ahmad, A.S.: Breaking visual CAPTCHAs with naive pattern recognition algorithms, 2007 Computer Security Applications Conference, pp (2007). [3] Chellapilla, K. and Simard, P.Y.: Using machine learning to break visual human interaction proofs (HIPs), Advances in Neural Infomation Processing Systems, Vol.17, pp (2005). [4] Elson, J., Douceur, J.R., Howell, J. and Saul, J.: Asirra: A CAPTCHA that exploits interest-aligned manual image categorization, Proc. 14th ACM Conference on Computer and Communications Security, pp (2007). [5] Golle, P.: Machine learning attacks against the asirra CAPTCHA, Proc. 15th ACM Conference on Computer and Communications Security, pp (2008). [6] Microsft: Microsoft Microsoft [7] Mori, G. and Malik, J.: Recognizing Objects in Adversarial Clutter: Breaking a Visual CAPTCHA, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 03 ), Vol.1, pp (2003). [8] Moy, G., Jones, N., Harkless, C. and Potter, R.: Distortion Estimation Techniques in Solving Visual CAPTCHAs, Proc IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 04 ), Vol.2, pp (2004). [9] Von Ahn, L., Maurer, B., McMillen, C., Abraham, D. and Blum, M.: recaptcha: Human-based character recognition via Web security measures, Science, Vol.321, No.5895, pp (2008). [10] 4 CAPTCHA IPSJ SIG Technical Report, Vol.2011-CSEC-52, No.13, pp.1 8 (2011). [11] Gossweiler, R., Kamvar, M. and Baluja, S.: What s Up CAPTCHA?: A CAPTCHA Based on Image Orientation, Proc. 18th International Conference on World c 2015 Information Processing Society of Japan 816
![, Hopper, N.J. and Langford, J.: CAPTCHA: Telling humans and computers apart, Advances in Cryptology, Eurocrypt 03, Vol.2656 of Lecture Notes in Computer Science, pp.294 311 (2003). [2] Yan, J.](/docs-images/42/22825307/images/page_9.jpg "and El Ahmad, A.S.: Breaking visual CAPTCHAs with naive pattern recognition algorithms, 2007 Computer Security Applications Conference, pp.279 291 (2007). [3] Chellapilla, K. and Simard, P.Y.")
10 Wide Web (WWW ), pp , ACM (2009). [12] Zhu, B.B., Yan, J., Li, Q., et al.: Attacks and Design of Image Recognition CAPTCHAs, ACM Conference on Computer and Communications Security (ACM CCS) 2010, pp (2010). [13] Almazyad, A.S., Ahmad, Y. and Kouchay, S.A.: Multimodal captcha: A user verification scheme, 2011 International Conference on Information Science and Applications (ICISA), pp.1 7 (2011). [14] Ahn, Y.C., Kim, N. and Kim, Y.: A User-friendly Image- Text Fusion CAPTCHA for Secure Web Services, Proc. International Conference on Information Integration and Web-based Applications and Services, p.550 (2013). [15] 49 Unix Magazine if0201.pdf [16] Vol.46, No.8, pp (2005). [17] Hayashi, E., Dhamija, R., Christin, N. and Perrig, A.: Use Your Illusion: Secure Authentication Usable Anywhere, Symposium on Usable Privacy and Security (SOUPS), pp.35 45, ACM (2008). [18] NuCAPTCHA: NuCAPTCHA (online), available from (accessed ). [19] CAPTCHA 2011 pp (2011). [20] CAPTCHA The Institute of Electronics, Information and Communication Engineers, The 29th Symposium on Cryptography and Information Security, p.1 (2012). [21] WordNet WordNet [22] NIST Special Publication Version Electronic Authentication Guideline, National Institute of Standards and Technology (2006). SP (2007) [23] for ipad device/speechtrainingdevice.html [24] Google Google [25] Yan, J. and Ahmad, A.S.E.: Usability of CAPTCHAs or usability issues in CAPTCHA design, Proc. 4th Symposium on Usable Privacy and Security, pp (2008). [26] Pixabay: Pixabay (online), available from (accessed ). [27] 06/06/105/ [28] Matthews, P. and Zou, C.C.: Scene tagging: Imagebased CAPTCHA using image composition and object relationships, Proc. 5th ACM Symposium on Information, Computer and Communications Security, pp (2010) Association for Computing Machinery c 2015 Information Processing Society of Japan 817
![1 7 (2011). [14] Ahn, Y.C., Kim, N. and Kim, Y.: A User-friendly Image- Text Fusion CAPTCHA for Secure Web Services, Proc.](/docs-images/42/22825307/images/page_10.jpg "International Conference on Information Integration and Web-based Applications and Services, p.550 (2013). [15] 49 Unix Magazine http://www.pitecan.com/unixmagazine/pdf/ if0201.pdf 2014-09-09.")
11 IEEE c 2015 Information Processing Society of Japan 818
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