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README.md

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+## Adversarial Examples for improving the robustness of Eye-State Classification 👁 👁 :
+
+### First Aim:
+Project aims to improve the robustness of the model by adding the adversarial examples to the training dataset.
+We investigated that the robustness of the models on the clean test data are always better than the attacks even though added the pertubated data to the training data.
+### Second Aim:
+
+Using adversarial examples, the project aims to improve the robustness and accuracy of a machine learning model which detects the eye-states against small perturbation of an image and to solve the misclassification problem caused by natural transformation.
+### Methodologies
+
+* Develop Wide Residual Network and Parseval Network.
+* Train Neural Networks using training dataset.
+* Construct the AEs using FGSM and Random Noise.
+#### The approach for the first aim.
+===================================================================
+* Train Neural Networks by adding Adversarial Examples (AEs) to the training dataset.
+* Evaluate the models on the original test dataset.
+
+#### The approach for the second aim.
+===================================================================
+* Train Neural Networks using Adversarial Training with AEs.
+* Attack the new model with different perturbated test dataset.
+
+### Neural Network Models
+
+#### Wide Residual Network
+
+* Baseline of the Model
+
+#### Parseval Network
+
+* [Orthogonality Constraint in Convolutional Layers](/src/models/Parseval_Networks/constraint.py)
+* [Convexity Constraint in Aggregation Layers](/src/models/Parseval_Networks/convexity_constraint.py)
+
+#### Convolutional Neural Network
+
+#### Adversarial Examples
+
+##### Fast Gradient Sign Method
+[Examples](src/visualization/Adversarial_Images.ipynb)
+
+### Evaluation
+
+* To evaluate the result of the neural network, Signal to Noise Ratio (SNR) is used as metric.
+* Use transferability of AEs to evaluate the models.
+
+## Development 
+
+#### Models:
+
+``` bash
+
+adversarial_examples_parseval_net/src/models
+├── FullyConectedModels
+│   ├── model.py
+│   └── parseval.py
+├── Parseval_Networks
+│   ├── constraint.py
+│   ├── convexity_constraint.py
+│   ├── parsevalnet.py
+├── _utility.py
+└── wideresnet
+    └── wresnet.py
+
+
+```
+
+### Final Results:
+
+* [The results of the first approach with FGSM](logs/AEModels/)
+* [The results of the first approach with Random Noise](logs/RandomNoisemodels/)
+* [The results of the second approach](logs/images)
+
+
+References
+============
+[1] Cisse, Bojanowski, Grave, Dauphin and Usunier, Parseval Networks: Improving Robustness to Adversarial Examples, 2017.
+
+[2] Zagoruyko and Komodakis, Wide Residual Networks, 2016.
+
+``` 
+
+@misc{ParsevalNetworks,
+  author= "Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, Nicolas Usunier"
+  title="Parseval Networks: Improving Robustness to Adversarial Examples"
+  year= "2017"
+}
+```
+
+``` 
+
+@misc{Wide Residual Networks
+  author= "Sergey Zagoruyko, Nikos Komodakis"
+  title= "Wide Residual Networks"
+  year= "2016"
+}
+```
+
+### Author
+
+Sefika Efeoglu
+
+Research Project, Data Science MSc, University of Potsdam
+