Popular GitHub Repositories for Pgd Attack

This work is based on enhancing the robustness of targeted classifier models against adversarial attacks. To achieve this, a convolutional autoencoder-based approach is employed that effectively counters adversarial perturbations introduced to the input images.

Evaluating CNN robustness against various adversarial attacks, including FGSM and PGD.

Implementation of PGD attack on a model trained on cifar10 dataset in TensorFlow. Also, FID between original images and generated images has been calculated.

A classical or convolutional neural network model with adversarial defense protection

Adversarial Network Attacks (PGD, pixel, FGSM) Noise on MNIST Images Dataset using Python (Pytorch)

Developed robust image classification models to prevent the effect of adversarial attacks

"Neural Computing and Applications" Published Paper (2023)

A classical-quantum or hybrid neural network with adversarial defense protection