Breaking Text-Based CAPTCHA with Sparse Convolutional Neural Networks

Ferreira, Diogo; Luis, Leira; Mihaylova, Petya; Georgieva, Pétia

doi:10.1007/978-3-030-31321-0_35

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…A proposed sparsity-integrated CNN used constraints to deactivate the fully connected connections in CNN. It ultimately increased the accuracy results compared to transfer learning, and simple CNN solutions ( Ferreira et al, 2019 ).…”

Section: Literature Reviewmentioning

confidence: 99%

A novel CAPTCHA solver framework using deep skipping Convolutional Neural Networks

Lu¹,

Huang²,

Meraj

et al. 2022

PeerJ Computer Science

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A Completely Automated Public Turing Test to tell Computers and Humans Apart (CAPTCHA) is used in web systems to secure authentication purposes; it may break using Optical Character Recognition (OCR) type methods. CAPTCHA breakers make web systems highly insecure. However, several techniques to break CAPTCHA suggest CAPTCHA designers about their designed CAPTCHA’s need improvement to prevent computer vision-based malicious attacks. This research primarily used deep learning methods to break state-of-the-art CAPTCHA codes; however, the validation scheme and conventional Convolutional Neural Network (CNN) design still need more confident validation and multi-aspect covering feature schemes. Several public datasets are available of text-based CAPTCHa, including Kaggle and other dataset repositories where self-generation of CAPTCHA datasets are available. The previous studies are dataset-specific only and cannot perform well on other CAPTCHA’s. Therefore, the proposed study uses two publicly available datasets of 4- and 5-character text-based CAPTCHA images to propose a CAPTCHA solver. Furthermore, the proposed study used a skip-connection-based CNN model to solve a CAPTCHA. The proposed research employed 5-folds on data that delivers 10 different CNN models on two datasets with promising results compared to the other studies.

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Section: Literature Reviewmentioning

confidence: 99%

A novel CAPTCHA solver framework using deep skipping Convolutional Neural Networks

Lu¹,

Huang²,

Meraj

et al. 2022

PeerJ Computer Science

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show abstract

“…In 2014 Google announced that today's Artificial Intelligence technology can solve even the most difficult variant of distorted text at 99.8% accuracy [111] and moved to a CAPTCHA scheme based on behavioral analysis that is considered the dominant CAPTCHA scheme in the market today. In the academic world, many works have shown the vulnerability of the traditional CAPTCHA schemes, nevertheless, many researchers still aim at breaking traditional CAPTCHA schemes and evaluating their security and usability [12,37,130,142], ignoring the emerging CAPTCHA schemes that have not been broken yet. Still, recent works in the literature do not consider these new CAPTCHA schemes neither in their review nor in their security evaluation [26,134,140].…”

Section: Introduction a Completely Automated Public Turing Tests To T...mentioning

confidence: 99%

Gotta CAPTCHA ’Em All: A Survey of 20 Years of the Human-or-computer Dilemma

GuerarMeriem¹,

VerderameLuca²,

Migliardi³

et al. 2021

ACM Comput. Surv.

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A recent study has found that malicious bots generated nearly a quarter of overall website traffic in 2019 [102]. These malicious bots perform activities such as price and content scraping, account creation and takeover, credit card fraud, denial of service, and so on. Thus, they represent a serious threat to all businesses in general, but are especially troublesome for e-commerce, travel, and financial services. One of the most common defense mechanisms against bots abusing online services is the introduction of Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), so it is extremely important to understand which CAPTCHA schemes have been designed and their actual effectiveness against the ever-evolving bots. To this end, this work provides an overview of the current state-of-the-art in the field of CAPTCHA schemes and defines a new classification that includes all the emerging schemes. In addition, for each identified CAPTCHA category, the most successful attack methods are summarized by also describing how CAPTCHA schemes evolved to resist bot attacks, and discussing the limitations of different CAPTCHA schemes from the security, usability, and compatibility point of view. Finally, an assessment of the open issues, challenges, and opportunities for further study is provided, paving the road toward the design of the next-generation secure and user-friendly CAPTCHA schemes.

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Intelligence in cyberspace: the road to cyber singularity

Priyadarshini

Cotton

2020

Journal of Experimental & Theoretical Artificial Intelligen

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Breaking Text-Based CAPTCHA with Sparse Convolutional Neural Networks

Cited by 5 publications

References 13 publications

A novel CAPTCHA solver framework using deep skipping Convolutional Neural Networks

A novel CAPTCHA solver framework using deep skipping Convolutional Neural Networks

Gotta CAPTCHA ’Em All: A Survey of 20 Years of the Human-or-computer Dilemma

Intelligence in cyberspace: the road to cyber singularity

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