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A stochastic input pre-processing technique based on a process of down-sampling/up-sampling using convolution and transposed convolution layers. Defending convolutional neural network against adversarial attacks.

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Stochastic-Input-Transformation

A stochastic input pre-processing technique based on a process of down-sampling/up-sampling using convolution and transposed convolution layers. Defending convolutional neural network against adversarial attacks.

A Pytorch code for our paper. It includes the implementation of our defense startegy named "SIT: Stochastic Input Transformation to Defend Against Adversarial Attacks on Deep Neural Networks"

If you find this code useful in your research, please cite:

@ARTICLE{9422778,
  author={Guesmi, Amira and Alouani, Ihsen and Baklouti, Mouna and Frikha, Tarek and Abid, Mohamed},
  journal={IEEE Design   Test}, 
  title={SIT: Stochastic Input Transformation to Defend Against Adversarial Attacks on Deep Neural Networks}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/MDAT.2021.3077542}}

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A stochastic input pre-processing technique based on a process of down-sampling/up-sampling using convolution and transposed convolution layers. Defending convolutional neural network against adversarial attacks.

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