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CAPTCHA

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.

Results

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

Documentation

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

Advisor

Acknowledgements

  • nlACh [Help regarding data uploading]
  • 41x3n [Help regarding data uploading]
   Frequently Asked Questions
  • Are these the only notebooks?

  • Do we need to download the data?

    • No, it is automatically downloaded, you just need to plug and play for getting the job done in Google Collaboratory.
  • Training time is taking too long?

    • Yes, some of the CAPTCHAs really take long time to train, (over 10 hrs for just 10 epochs even in GPUs). It is good to have multiple GPUs when you are using this on your own machine.
  • Found a bug? or version issue?

    • PRs welcome, fork it, and send a pull request!

BibTeX and citations

@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}
}