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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		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%

Documentation

[Thesis - Deceiving computers in Reverse Turing Test through Deep Learning] | [Slides]

Advisor

• Dripta Mj

Acknowledgements

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

   Frequently Asked Questions

• Are these the only notebooks?

  • No, https://colab.research.google.com/github/Jimut123/CAPTCHA/blob/master/pyfiles/sphinx/sphinx_33_10e_9873.ipynb is the path for testing the notebooks in Colab, please use this format for testing other notebooks, there are some awesome visualizations too...

• 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}
}