Breaking down Captcha using edge corners and fuzzy logic segmentation/recognition technique

Nachar, Rabih Al; Inaty, Elie; Bonnin, Patrick; Alayli, Yasser

doi:10.1002/sec.1316

Cited by 13 publications

(6 citation statements)

References 27 publications

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“…Such that it divides a static text-CAPTCHA image into different sequential timesteps. This idea succeeds the pre-segmentation process used in earlier works, like in [24]. The paper also implements a forward-backward connectionist temporal classification (CTC) function to align the sequential distribution of the output from the 2DRNN and return the probability of the corresponding text character.…”

Section: Single-view Learning Approachmentioning

confidence: 74%

Multiview deep learning-based attack to break text-CAPTCHAs

Yusuf

Srivastava

Singh

et al. 2022

Int. J. Mach. Learn. & Cyber.

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Completely Automated Public Turing Test To Tell Computer and Humans Apart (CAPTCHA) is a computer program that prevents malicious computer users. Text-CAPTCHA schemes utilize less-computational costs. Hence, they are the most popularly used. This paper investigates the effectiveness of state-of-the-art (SOTA) text-CAPTCHA schemes, proposes a Multiview deep learning system to break them, and highlights their weaknesses. Rather than the usual single-view feature extraction, the proposed model explores correlational features from multiple views to increase the model’s generalization and classification accuracy. The model combines convolutional neural networks and recurrent networks to preserve the input text-CAPTCHA’s spatial and sequential order. The proposed system has successfully achieved average accuracies ranging from 93.6% to 100%, and the average time to break a text-CAPTCHA scheme ranges from 0.0032 to 0.21 seconds on eight different datasets. Furthermore, an ablation study on 71 human users was conducted to evaluate the effectiveness of the schemes. The results demonstrated that the proposed system effectively outperforms the human users whom the schemes were designed to serve. Lastly, when compared with existing systems, the proposed system outperforms existing SOTA systems with an accuracy gap of almost 40% higher.

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Section: Single-view Learning Approachmentioning

confidence: 74%

Multiview deep learning-based attack to break text-CAPTCHAs

Yusuf

Srivastava

Singh

et al. 2022

Int. J. Mach. Learn. & Cyber.

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“…Reference [7] tried to connect connection edge points between two merged characters and determined the optimal segmentation line by confidence, as shown in Figure 6.…”

Section: Segmentation Methods Based On Character Contourmentioning

confidence: 99%

“…QuadIV QuadIII Figure 6: The optimal segmentation line in [7]. scaling and affine transformation, it is widely used in face recognition, CAPTCHA recognition, shape matching, and other fields.…”

Section: Quadii Quadimentioning

confidence: 99%

A Survey on Breaking Technique of Text-Based CAPTCHA

Chen

Luo²,

Guo

et al. 2017

Security and Communication Networks

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The CAPTCHA has become an important issue in multimedia security. Aimed at a commonly used text-based CAPTCHA, this paper outlines some typical methods and summarizes the technological progress in text-based CAPTCHA breaking. First, the paper presents a comprehensive review of recent developments in the text-based CAPTCHA breaking field. Second, a framework of text-based CAPTCHA breaking technique is proposed. And the framework mainly consists of preprocessing, segmentation, combination, recognition, postprocessing, and other modules. Third, the research progress of the technique involved in each module is introduced, and some typical methods of segmentation and recognition are compared and analyzed. Lastly, the paper discusses some problems worth further research.

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“…Moy et al [10] developed distortion estimation techniques to attack EZ-Gimpy of CMU with a success rate of 99% and four-letter Gimpy-r with a success rate of 78%. Nachar et al [11] used edge corners and fuzzy logic segmentation/ recognition technique to break the CAPTCHAs of eBay, Wikipedia, reCAPTCHA, Yahoo!, and the success rates were 68.2%, 76.7%, 62.5% and 57.3% respectively.…”

Section: Related Workmentioning

confidence: 99%

An Attack on Hollow CAPTCHA Using Accurate Filling and Nonredundant Merging

Luo¹,

Hu²,

et al. 2018

IETE Technical Review

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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.

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Breaking down Captcha using edge corners and fuzzy logic segmentation/recognition technique

Cited by 13 publications

References 27 publications

Multiview deep learning-based attack to break text-CAPTCHAs

Multiview deep learning-based attack to break text-CAPTCHAs

A Survey on Breaking Technique of Text-Based CAPTCHA

An Attack on Hollow CAPTCHA Using Accurate Filling and Nonredundant Merging

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