An ASR (Automatic Speech Recognition) adversarial attack repository.
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Updated
Nov 7, 2023 - Jupyter Notebook
An ASR (Automatic Speech Recognition) adversarial attack repository.
vanilla training and adversarial training in PyTorch
Individual Study in Computer Architecture and Systems Laboratory (CASYS) with Prof.Jaehyuk Huh in 2021 Summer
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.
Adversarial Sample Generation
Adversarial Network Attacks (PGD, pixel, FGSM) Noise on MNIST Images Dataset using Python (Pytorch)
Adversarial attacks on CNN using the FSGM technique.
A classical or convolutional neural network model with adversarial defense protection
An University Project for the AI4Cybersecurity class.
Adversarial attacks on a deep neural network trained on ImageNet
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 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.
Implementations for several white-box and black-box attacks.
Learning Adversarial Robustness in Machine Learning both Theory and Practice.
Developed robust image classification models to prevent the effect of adversarial attacks
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.
A classical-quantum or hybrid neural network with adversarial defense protection
This project demonstrates adversarial attacks on deep neural networks trained on the CIFAR-10 dataset
This repository contains the codebase for Jailbreaking Deep Models, which investigates the vulnerability of deep convolutional neural networks to adversarial attacks. The project systematically implements and analyzes Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and localized patch-based attacks on the pretrained
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