imrahulr/adversarial_robustness_pytorch
Unofficial implementation of the DeepMind papers "Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples" & "Fixing Data Augmentation to Improve Adversarial Robustness" in PyTorch
This project helps machine learning researchers and practitioners evaluate and improve the security of their image classification models. It takes an existing image classification model and training data, and then it can either train the model to be more resistant to 'adversarial attacks' or test how well it performs against various attack methods. The output is a more robust model or metrics showing its resilience.
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Use this if you are developing or deploying image recognition systems and need to ensure they are not easily fooled by subtle, malicious changes to input images.
Not ideal if you are not working with image data, or if your primary concern is model accuracy without considering adversarial robustness.
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99
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12
Language
Python
License
MIT
Category
Last pushed
Mar 04, 2022
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