Adversarial Attacks on Image data

Adversarial attacks to SRNet

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.

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.

Adversarial attacks on CNN using the FSGM technique.

Adversarial-Attacks-and-Defence

This study was conducted in collaboration with the University of Prishtina (Kosovo) and the University of Oslo (Norway). This implementation is part of the paper entitled "Attack Analysis of Face Recognition Authentication Systems Using Fast Gradient Sign Method", published in the International Journal of Applied Artificial Intelligence by Taylo…

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

Adversarial Sample Generation

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.

Adversarial attacks on a deep neural network trained on ImageNet

Notebook to implement different approaches for Adversarial Attack using Python and PyTorch.

A classical or convolutional neural network model with adversarial defense protection

An University Project for the AI4Cybersecurity class.

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

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

Individual Study in Computer Architecture and Systems Laboratory (CASYS) with Prof.Jaehyuk Huh in 2021 Summer

Implementations for several white-box and black-box attacks.

Learning Adversarial Robustness in Machine Learning both Theory and Practice.

An ASR (Automatic Speech Recognition) adversarial attack repository.