ByungKwanLee/Masking-Adversarial-Damage
[CVPR 2022] Official PyTorch Implementation for "Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network"
This project helps machine learning engineers and researchers optimize deep learning models used in image recognition for real-world reliability. It takes a pre-trained neural network and image datasets, then identifies and removes parts of the model that are vulnerable to adversarial attacks, resulting in a smaller, more robust model that maintains its accuracy against malicious inputs. This is useful for anyone deploying computer vision systems where security and performance are critical.
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Use this if you need to make your image classification models more resilient to adversarial attacks while also reducing their size for efficiency.
Not ideal if your primary concern is solely model accuracy without regard for adversarial robustness or model compression.
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32
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4
Language
Python
License
MIT
Category
Last pushed
Mar 13, 2023
Commits (30d)
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