https://github.com/emaballarin/carso

👀🛡️ Code for the paper “Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness” by Emanuele Ballarin, Alessio Ansuini and Luca Bortolussi (2024)
https://github.com/emaballarin/carso

adversarial-defense adversarial-machine-learning adversarial-robustness artificial-intelligence artificial-neural-networks cognitive-computing deep-learning deep-neural-networks machine-learning neural-network neural-networks neural-representation neural-representations neuro-inspired robust-machine-learning robustness variational-autoencoder variational-method

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👀🛡️ Code for the paper “Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness” by Emanuele Ballarin, Alessio Ansuini and Luca Bortolussi (2024)

• Host: GitHub
• URL: https://github.com/emaballarin/carso
• Owner: emaballarin
• License: mit
• Created: 2022-09-03T01:43:38.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-23T03:50:11.000Z (7 months ago)
• Last Synced: 2024-05-23T04:55:36.695Z (7 months ago)
• Topics: adversarial-defense, adversarial-machine-learning, adversarial-robustness, artificial-intelligence, artificial-neural-networks, cognitive-computing, deep-learning, deep-neural-networks, machine-learning, neural-network, neural-networks, neural-representation, neural-representations, neuro-inspired, robust-machine-learning, robustness, variational-autoencoder, variational-method
• Language: Python
• Homepage: https://bucket.ballarin.cc/aimldl/carso2024cnn/paper/carso_2024_05_paper_final.pdf
• Size: 18.6 KB
• Stars: 3
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# *Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness*

Code for model training, evaluation, and reproduction of the experiments contained in the [paper *“Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness”*](https://bucket.ballarin.cc/aimldl/carso2024cnn/paper/carso_2024_05_paper_final.pdf) by [Emanuele Ballarin](https://ballarin.cc), [Alessio Ansuini](https://www.areasciencepark.it/piattaforme-tecnologiche-update-2023/il-team/alessio-ansuini/) and [Luca Bortolussi](https://ai-lab.units.it/?page_id=139) (2024).

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*Instructions for reproducibility* can be found in [REPRO.md](./REPRO.md)