An Attack on Hollow CAPTCHA Using Accurate Filling and Nonredundant Merging. (25th December 2018)

• Record Type: Journal Article
• Authors: Chen, Jun; Luo, Xiangyang; Hu, Jianwei; Ye, Dengpan; Gong, Daofu
• Is Part Of: IETE technical review. Volume 35(2018)Supplement 1
• Page Start: 106
• Page End: 118
• Publication Date: 2018-12-25
• Subjects: Privacy protection -- multimedia security -- big data -- Hollow CAPTCHA -- Attack -- Inner-outer contour -- Minimum-nearest neighbor; Telecommunication -- Periodicals; Electronics -- Periodicals; Electronics; Telecommunication; Periodicals; 621.38
• DOI: 10.1080/02564602.2018.1520152
• Languages: English
• ISSNs: 0256-4602

Abstract

ABSTRACT: As one of the effective access control mechanism, CAPTCHA can provide privacy protection and multimedia security for big data. In this paper, an attack on hollow CAPTCHA using accurate filling and nonredundant merging is proposed. Firstly, a thinning operation is introduced to repair character contour precisely. Secondly, an inner-outer contour filling algorithm is presented to acquire solid characters, which only fills the hollow character components rather than noise blocks. Thirdly, the segmentation to the solid characters produces mostly individual characters and only a small number of character components. Fourthly, a minimum-nearest neighbor merging algorithm is proposed to obtain individual characters without redundancy. Lastly, the convolutional neural network (CNN) is applied to acquire the final recognition results. The experimental results based on the real CAPTCHA data sets show that comparing with the existing attack methods on hollow CAPTCHA, the proposed method has higher success rate and superior efficiency.