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AVID: Adversarial Visual Irregularity Detection
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ground_truth.pkl [Add] - ground Trut Apr 13, 2019 [Add] main code , train & test dataset , network structures Mar 20, 2019

AVID: Adversarial Visual Irregularity Detection

This code repository includes the source code for the Paper:

Mohammad Sabokrou, Masoud Pourreza, Mohsen Fayyaz, Rahim Entezari, Mahmood Fathy, Jürgen Gall, Ehsan Adeli
ACCV 2018 : Asian Conference on Computer Vision

The experimentation framework is based on PyTorch;Ir_Mnist Dataset is available in train_mnist as train set and test_mnist_matrix as test set.

The source code and dataset (MultiMNIST) are released under the MIT License. See the License file for details.

Requirements and References

The code uses the following Python packages and they are required: sklearn, pytorch, numpy, torchvision, scipy, Pillow

The code is only tested in Python 3 and Pytorch 1.0.0

We adapt and use some code snippets from:


For any question, please contact to


If you use this codebase or any part of it for a publication, please cite:

  title={AVID: Adversarial Visual Irregularity Detection},
  author={Sabokrou, Mohammad and Pourreza, Masoud and Fayyaz, Mohsen and Entezari, Rahim and Fathy, Mahmood and Gall, J{\"u}rgen and Adeli, Ehsan},
  journal={arXiv preprint arXiv:1805.09521},

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