Notebook to implement different approaches for Adversarial Attack using Python and PyTorch.
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Updated
Feb 24, 2024 - Jupyter Notebook
Notebook to implement different approaches for Adversarial Attack using Python and PyTorch.
A Comprehensive Study on Cloud-Based Model Interpretability, Accountability, and Privacy in Machine Learning with Resilience to Adversarial Attacks
A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.
adversarial attack on malware detector
Adversarial Attack on 3D U-Net model: Brain Tumour Segmentation.
A Tensorflow adversarial machine learning attack toolkit to add perturbations and cause image recognition models to misclassify an image
Implementation of Papers on Adversarial Examples
ECE C147: Neural Networks & Deep Learning. Repository for "Developing Robust Networks to Defend Against Adversarial Examples". Implementing adversarial data augmentation on CNNs and RNNs.
SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
This repository contains the implementation of two adversarial example attack methods FGSM, IFGSM and one Input Transformation defense mechanism against all attacks using Imagenet dataset.
Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.
In this work, we extend the FGSM method proposing multistep adversarial perturbation (MSAP) procedures to study the recommenders’ robustness under powerful methods. Letting fixed the perturbation magnitude, we illustrate that MSAP is much more harmful than FGSM in corrupting the recommendation performance of BPR-MF.
Adversarial Attacks on MNIST
Adversarially-robust Image Classifier
Adversarial attacks to SRNet
Fast Gradient Sign Method Adversarial Attack on Digit Recognition Model
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