initial code base

.gitignore

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-# Byte-compiled / optimized / DLL files
-__pycache__/
+.idea/
+*__pycache__*
+.DS_Store
 *.py[cod]
-*$py.class
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-# C extensions
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-# Distribution / packaging
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-.installed.cfg
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-MANIFEST
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+*.xml
+venv/

README.md

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-# RobustSNNConversion
-Adversarially Robust Spiking Neural Networks Through Conversion
+# Adversarially Robust Spiking Neural Networks Through Conversion
 
-Repository will be updated soon..
+This is the code repository of the following [paper](https://arxiv.org/pdf/2311.09266.pdf) to perform adversarially robust ANN-to-SNN conversion.
+
+"Adversarially Robust Spiking Neural Networks Through Conversion"\
+<em>Ozan Özdenizci, Robert Legenstein</em>\
+arXiv preprint arXiv:2311.09266 (2023).
+
+## Reference
+If you use this code or models in your research and find it helpful, please cite the following paper:
+```
+@article{ozdenizci2023adversarially,
+  title={Adversarially robust spiking neural networks through conversion},
+  author={Ozan {\"O}zdenizci and Robert Legenstein},
+  journal={arXiv preprint arXiv:2311.09266},
+  year={2023}
+}
+```
+
+## Acknowledgments
+
+Authors of this work are affiliated with Graz University of Technology, Institute of Theoretical Computer Science, and Silicon Austria Labs, TU Graz - SAL Dependable Embedded Systems Lab, Graz, Austria. This work has been supported by the "University SAL Labs" initiative of Silicon Austria Labs (SAL) and its Austrian partner universities for applied fundamental research for electronic based systems.
+
+Parts of this code repository is based on the following works:
+
+* https://github.com/nitin-rathi/hybrid-snn-conversion
+* https://github.com/putshua/SNN-RAT

attack/__init__.py

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+from attack.fgsm import FGSM
+from attack.rfgsm import RFGSM
+from attack.pgd import PGD
+from attack.tpgd import TPGD
+from attack.mart import MART
+from attack.apgd import APGD
+from attack.apgdt import APGDT
+from attack.square import Square
+from attack.ensemble import Ensemble