This work is one of a series of three distinct repositories that collectively constitute the coursework focused on Neural Networks for the Data Science & Information Technologies masters course: Μ124 - Machine Learning at the National and Kapodistrian University of Athens (NKUA) during the Fall 2022 semester. The two other repositories deal with:
This specific repository contains code to solve different tasks related to building, training and creating adversarial examples for classification models on the MNIST and CIFAR10 datasets.
The following were done
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Two Convolutional Neural Network (CNN) models to classify iamges in the MNIST and CIFAR10 datasets with test accuracy of
$0.99%$ and$0.80%$ respectively. -
A generator class able to create fake images from noise for both datasets
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A composite model class that combines the generator and the dataset-specific frozen CNN classifier model. This composite model generates adversarial examples in the form of fake images that resemble the real training images but with added noise to mislead the model into misclassifying them as a different target class.
