Simple and Easy: Transfer Learning-Based Attacks to Text CAPTCHA

Wang, Ping; Gao, Haichang; Shi, Ziyu; Yuan, Zhongni; Hu, Jiangping

doi:10.1109/access.2020.2982945

Cited by 24 publications

(10 citation statements)

References 30 publications

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“…They are concatenated to form a single view and then continue with training [40]. Several studies like [9,25,36,45] have considered the single-view learning approach to defeat text-CAPTCHA schemes. This section discusses some of the single-view learning approaches related to the work described in this paper.…”

Section: Single-view Learning Approachmentioning

confidence: 99%

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: 99%

Multiview deep learning-based attack to break text-CAPTCHAs

Yusuf

Srivastava

Singh

et al. 2022

Int. J. Mach. Learn. & Cyber.

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“…The authors in [34,35] used a segmentation-free model that combines CNN and attention-based recurrent neural network (RNN) to accomplish CAPTCHA recognition. The CNN part extracts features from a CAPTCHA image and produces feature vectors, and an Long Short-Term Memory (LSTM) network transforms the feature vectors into a text sequence.…”

Section: Related Workmentioning

confidence: 99%

CAPTCHA Recognition Using Deep Learning with Attached Binary Images

et al. 2020

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Websites can increase their security and prevent harmful Internet attacks by providing CAPTCHA verification for determining whether end-user is a human or a robot. Text-based CAPTCHA is the most common and designed to be easily recognized by humans and difficult to identify by machines or robots. However, with the dramatic advancements in deep learning, it becomes much easier to build convolutional neural network (CNN) models that can efficiently recognize text-based CAPTCHAs. In this study, we introduce an efficient CNN model that uses attached binary images to recognize CAPTCHAs. By making a specific number of copies of the input CAPTCHA image equal to the number of characters in that input CAPTCHA image and attaching distinct binary images to each copy, we build a new CNN model that can recognize CAPTCHAs effectively. The model has a simple structure and small storage size and does not require the segmentation of CAPTCHAs into individual characters. After training and testing the proposed CAPTCHA recognition CNN model, the achieved experimental results reveal the strength of the model in CAPTCHA character recognition.

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“…[34] and Wang et al. [10]. A human‐based attack is simpler and less costly because a human solver earns $0.75 per 1000 CAPTCHAs solved in an hour [8].…”

Section: Introductionmentioning

confidence: 99%

Detecting human attacks on text‐based CAPTCHAs using the keystroke dynamic approach

Alsuhibany

Alreshoodi

2021

IET Information Security

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A Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is a simple test that is used on websites to differentiate between human users and automated attacks that indulge in spamming and other fraudulent activities. A text-based CAPTCHA is the most popular security technique used by many websites on the Internet, such as Microsoft, Google and eBay, to secure their sites from automated attacks. By design, however, a CAPTCHA is unable to differentiate between a legitimate human user and a human-based attacker. This may make websites vulnerable to human-based attacks while using CAPTCHAs. Hence this article proposes a novel defence system using the keystroke dynamic approach. To evaluate our system, a laboratory experiment was conducted and the results showed that the proposed system is able to detect human-based attacks on text-based CAPTCHAs effectively with a 100% detection rate. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Simple and Easy: Transfer Learning-Based Attacks to Text CAPTCHA

Cited by 24 publications

References 30 publications

Multiview deep learning-based attack to break text-CAPTCHAs

Multiview deep learning-based attack to break text-CAPTCHAs

CAPTCHA Recognition Using Deep Learning with Attached Binary Images

Detecting human attacks on text‐based CAPTCHAs using the keystroke dynamic approach

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