Popular GitHub Repositories for Fgsm 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.

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)

Adversarial attacks on CNN using the FSGM technique.

This repository contains the implementation of three adversarial example attacks including FGSM, noise, semantic attack and a defensive distillation approach to defense against the FGSM attack.

An University Project for the AI4Cybersecurity class.

Adversarial-Attacks-and-Defence

This project evaluates the robustness of image classification models against adversarial attacks using two key metrics: Adversarial Distance and CLEVER. The study employs variants of the WideResNet model, including a standard and a corruption-trained robust model, trained on the CIFAR-10 dataset. Key insights reveal that the CLEVER Score serves as

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

Adversarial Attacks on Image data

"Neural Computing and Applications" Published Paper (2023)

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

The Fast Gradient Sign Method (FGSM) combines a white box approach with a misclassification goal. It tricks a neural network model into making wrong predictions. We use this technique to anonymize images.