Neural network CAPTCHA crackers

This paper describes several experiments using deep neural networks to break character-based image CAPTCHAs. The goal of our research was to see if one could develop a single neural network capable of breaking all character-based, image CAPTCHAs. Our main deep neural net uses convolutional neural network layers followed by a dense layer, and a recurrent recurrent neural network layer instead of the conventional method of CAPTCHA breaking based on segmenting and recognizing individual letters. Our experiments with these networks were conducted using a synthetically generated dataset of CAPTCHAs which is independently useful for future research. We trained on both fixed-and variable-length CAPTCHAs and our main neural net configuration was able to achieve accuracy levels of 99.8% and 81%, respectively.

[1]  A. Wenger,et al.  Reading in the brain , 2010 .

[2]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[3]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[4]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  John Langford,et al.  CAPTCHA: Using Hard AI Problems for Security , 2003, EUROCRYPT.

[6]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[7]  Masae Sato,et al.  Reading in the brain , 2012 .

[8]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[9]  Geoffrey E. Hinton,et al.  On the importance of initialization and momentum in deep learning , 2013, ICML.

[10]  Patrice Y. Simard,et al.  Using Machine Learning to Break Visual Human Interaction Proofs (HIPs) , 2004, NIPS.

[11]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[12]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[13]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[14]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[15]  Jürgen Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[16]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Jitendra Malik,et al.  Recognizing objects in adversarial clutter: breaking a visual CAPTCHA , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[18]  Moni Naor,et al.  VERI CATION OF A HUMAN IN THE LOOP OR IDENTI CATION VIA THE TURING TEST , 1996 .

[19]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[20]  Yoshua Bengio,et al.  Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .

[21]  G. McConkie,et al.  The span of the effective stimulus during a fixation in reading , 1975 .

[22]  Alex Graves,et al.  Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.

[23]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[24]  Samy Bengio,et al.  Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Erich Elsen,et al.  Deep Speech: Scaling up end-to-end speech recognition , 2014, ArXiv.

[26]  Sepp Hochreiter,et al.  Untersuchungen zu dynamischen neuronalen Netzen , 1991 .

[27]  Yaroslav Bulatov,et al.  Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks , 2013, ICLR.

[28]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[29]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[30]  PAUL J. WERBOS,et al.  Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.

[31]  Michelle Becker,et al.  Perceptrons An Introduction To Computational Geometry , 2016 .