GiorgosKarantonis/Adversarial-Attacks-with-Relativistic-AdvGAN
Using relativism to improve GAN-based Adversarial Attacks. 🦾
This tool helps researchers and security engineers working with image classification models understand and generate 'adversarial examples.' It takes an image dataset and a trained classification model as input, then generates slightly modified versions of those images that can fool the model while still looking normal to a human eye. This is primarily for those exploring the vulnerabilities of deep learning models.
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Use this if you need to create visually similar, undetectable changes to images that can trick state-of-the-art image recognition systems, or evaluate the robustness of a trained model against such attacks.
Not ideal if you are looking to defend against adversarial attacks, as this project focuses on generating them rather than building robust models.
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44
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9
Language
Python
License
GPL-3.0
Category
Last pushed
Mar 24, 2023
Commits (30d)
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