Abstract
Image verification has been widely used in numerous websites to prevent them from batch registration or automated posting. One category of the image verification is generated by adding noise into character or digit images to make them hard to be recognized by Optical Character Recognition (OCR). In this paper, we propose a novel probability gradient function for active contour models to efficiently segment this type of images for easier recognition. Experiments on a set of images with different intensities and types of noise show the superiority of the proposed probability gradient to traditional method. The purpose of our paper is to warn some websites who are still using such kind of verification: they should improve their defense method to prevent them from the potential risk.
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References
Mori, G., Malik, J.: Recognizing Objects in Adversarial Clutter: Breaking a Visual CAPTCHA. In: Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 134–141 (2003)
Moy, G., Jones, N., Harkless, C.: Distortion Estimation Techniques in Solving Visual CAPTCHAs. In: Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 23–28 (2004)
Chellapilla, K., Larson, K., Simard, P., Czerwinski, M.: Computers Beat Humans at Single Character Recognition in Reading Based Human Interaction Proofs (HIPs). In: Proceeding. E-Mail and Anti-Spam (2005)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. Journal Computer Vision 22(1), 61–79 (1997)
Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Analysis and Machine Intelligence 17(2), 158–175 (1995)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. Journal Computer Vision 1, 321–331 (1987)
Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Processing 10(2), 266–277 (2001)
Luo, H., Lu, Q., Acharya, R., Gaborski, R.: Robust Snake Model. In: Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 1–6 (2002)
Chesnaud, C., Refregier, P., Boulet, V.: Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models. IEEE Trans. Pattern Analysis and Machine Intelligence 21(11), 1145–1157 (1999)
Martin, P., Refregier, P., Goudail, F., Guerault, F.: Influence of the Noise Model on Level Set Active Contour Segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence 26(6), 799–803 (2004)
Leventon, M., Grimson, W., Faugeras, O.: Statistical shape influence in geodesic active contours. In: Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 316–323 (2000)
Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, E., Willsky, A.: Model-based curve evolution technique for image segmentation. In: Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 463–468 (2001)
Cremers, D., Sochen, N., Schnorr, C.: A multiphase dynamic labeling model for variational recognition-driven image segmentation. Int. Journal Computer Vision 66(1), 67–81 (2006)
Lalit, G., Thotsapon, S.: A Gaussian mixture based image segmentation algorithm. Pattern Recognition 31(3), 315–325 (1998)
Henstock, P.V., Chelberg, D.M.: Automatic gradient threshold determination for edge detection. IEEE Trans. Image Processing 5(5), 784–787 (1996)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum-likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society Series B 39(1), 1–38 (1977)
Wu, C.F.: On the convergence properties of the EM algorithm. The Annals of Statistics 11(1), 95–103 (1983)
Li, C.M., Xu, C.Y., Gui, C.F., Fox, M.D.: Level Set Evolution Without Re-initialization: A New Variational Formulation. In: Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 430–436 (2005)
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Cai, L., Xu, Y., He, L., Zhao, Y., Yang, X. (2010). An Effective Segmentation for Noise-Based Image Verification Using Gamma Mixture Models. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12297-2_3
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DOI: https://doi.org/10.1007/978-3-642-12297-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12296-5
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