Survey on Captcha Recognition Using Deep Learning

Srivastava, Mohit; Shreya, Sakshi; Dutta, Sanghamitra; Ningthoujam, Chitrapriya

doi:10.1007/978-981-15-7394-1_26

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Typically, image-selection CAPTCHA instances are ASIRRA CAPTCHA, Facebook CAPTCHA and Google CAPTCHA, IMAGINATION [ 12 ], FR-CAPTCHA [ 13 ], ARTiFACIAL CAPTCHA [ 14 ], etc. To classify these images, the most widely used and effective methods for cracking image-based CAPTCHAs have been to train neural networks, with outstanding research by Cheung [ 15 ], Sivakorn et al [ 16 ], Zhu et al [ 17 ], Gao et al [ 18 ], Li [ 19 ], Srivastava et al [ 20 ], Fatmah et al [ 21 ], etc. Mouse-based CAPTCHAs require users to drag an image fragment back to its original location or adjust an image’s orientation, such as: What’s up CAPTCHA [ 22 ], Capy CAPTCHA [ 23 ], and Geetest CAPTCHA [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

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New Cognitive Deep-Learning CAPTCHA

Trong

Huong

Hoang

2023

Sensors

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CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), or HIP (Human Interactive Proof), has long been utilized to avoid bots manipulating web services. Over the years, various CAPTCHAs have been presented, primarily to enhance security and usability against new bots and cybercriminals carrying out destructive actions. Nevertheless, automated attacks supported by ML (Machine Learning), CNN (Convolutional Neural Network), and DNN (Deep Neural Network) have successfully broken all common conventional schemes, including text- and image-based CAPTCHAs. CNN/DNN have recently been shown to be extremely vulnerable to adversarial examples, which can consistently deceive neural networks by introducing noise that humans are incapable of detecting. In this study, the authors improve the security for CAPTCHA design by combining text-based, image-based, and cognitive CAPTCHA characteristics and applying adversarial examples and neural style transfer. Comprehend usability and security assessments are performed to evaluate the efficacy of the improvement in CAPTCHA. The results show that the proposed CAPTCHA outperforms standard CAPTCHAs in terms of security while remaining usable. Our work makes two major contributions: first, we show that the combination of deep learning and cognition can significantly improve the security of image-based and text-based CAPTCHAs; and second, we suggest a promising direction for designing CAPTCHAs with the concept of the proposed CAPTCHA.

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Section: Related Workmentioning

confidence: 99%

“…This model took first and second places in the 2014 ILSVRC challenge. ResNet, which stands for Residual Network, is a type of CNN introduced in 2015 by He Kaiming et al [ 20 ]. ResNet-101 has 101 layers and was created using convolution neural networks and residual blocks.…”

Section: Applied Techniquesmentioning

confidence: 99%

New Cognitive Deep-Learning CAPTCHA

Trong

Huong

Hoang

2023

Sensors

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“…It asks users to select images matching a category or label provided in the challenge instruction to verify that they are humans and not bots. For a more extensive review, see Chen et al (2017), Kaur and Behal (2014), Sinha andTarar (2016), Srivastava, Sakshi, Dutta, andNingthoujam (2021) and Tiwari (2018).…”

Section: Breaking Captchasmentioning

confidence: 99%