It is increasingly becoming difficult for human beings to work on their day to day life without going through the process of reverse Turing test, where the Computers tests the users to be humans or not. Almost every website and service providers today have the process of checking whether their website is being crawled or not by automated bots which could extract valuable information from their site. In the process the bots are getting more intelligent by the use of Deep Learning techniques to decipher those tests and gain unwanted automated access to data while create nuisance by posting spam. Humans spend a considerable amount of time almost every day when trying to decipher CAPTCHAs. The aim of this investigation is to check whether the use of a subset of commonly used CAPTCHAs, known as the text CAPTCHA is a reliable process for verifying their human customers. We mainly focused on the preprocessing step for every CAPTCHA which converts them in binary intensity and removes the confusion as much as possible and developed various models to correctly label as many CAPTCHAs as possible. We also suggested some ways to improve the process of verifying the humans which makes it easy for humans to solve the existing CAPTCHAs and difficult for bots to do the same.

We have build many models to solve some of the difficult open sourced CAPTCHAs that are available on the internet. We have obtained about more than 99.5% accuracy on most of the models, which converges at about 5 epochs. The generators folder have some of the modified codes that we have used to generate the data to feed into the model. The pyfiles folder section have all of the models and their corresponding python codes.

CAPTCHA name	CAPTCHA img	Algorithm used	Accuracy Obtained	Try out in Google Colab
JAM CAPTCHA		kNN	99.53%
CNN_c4l_16x16_550		CNN - modified CIFAR 10	99.91%
captcha-1L		Own CNN model - multilabel classification	99.67%
captcha_4_letter		LSTM model - multilabel classification	99.87%
captcha_v2		Own CNN - multilabel classification	90.102%
circle_captcha		Alex Net with multilabel classification	99.99%
faded		Alex Net with multilabel classification	99.44%
fish_eye		Alex Net with multilabel classification	99.46%
mini_captcha		Alex Net with multilabel classification	97.25%
multicolor		Alex Net with multilabel classification	95.69%
railway_captcha		Own CNN model	99.94%
sphinx		Alex Net with multilabel classification	99.62%

[Thesis - Deceiving computers in Reverse Turing Test through Deep Learning (Research paper)] | [Slides]

• Dripta Mj

• nlACh [Help regarding data uploading]
• 41x3n [Help regarding data uploading]

Please feel free to raise issues and fix any existing ones. Further details can be found in our code of conduct.

• Always start your PR description with "Fixes #issue_number", if you're fixing an issue.
• Briefly mention the purpose of the PR, along with the tools/libraries you have used. It would be great if you could be version specific.
• Briefly mention what logic you used to implement the changes/upgrades.
• Provide in-code review comments on GitHub to highlight specific LOC if deemed necessary.
• Please provide snapshots if deemed necessary.
• Update readme if required.

@article{DBLP:journals/corr/abs-2006-11373,
  author    = {Jimut Bahan Pal},
  title     = {Deceiving computers in Reverse Turing Test through Deep Learning},
  journal   = {CoRR},
  volume    = {abs/2006.11373},
  year      = {2020},
  url       = {https://arxiv.org/abs/2006.11373},
  archivePrefix = {arXiv},
  eprint    = {2006.11373},
  timestamp = {Tue, 23 Jun 2020 17:57:22  0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2006-11373.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}