This repository contains the implementation of three adversarial example attack methods FGSM, IFGSM, MI-FGSM and one Distillation as defense against all attacks using MNIST dataset.

Ensemble Adversarial Black-Box Attacks against Deep Learning Systems Trained by MNIST, USPS and GTSRB Datasets

Morphence: An implementation of a moving target defense against adversarial example attacks demonstrated for image classification models trained on MNIST and CIFAR-10.

PyTorch adversarial attack baselines for ImageNet, CIFAR10, and MNIST (state-of-the-art attacks comparison)

论文 Hinton等的论文 Matrix capsules with EM routing - Hinton, G. E., Sabour, S. and Frosst, N. (2018) Dynamic Routing Between Capsules - Sabour, S., Frosst, N. and Hinton, G.E. (2017) Transforming Auto-encoders - Hinton, G. E., Krizhevsky, A. and Wang, S. D. (2011) A parallel computation that assigns canonical object-based frames of reference. - Hinton, G.E. (1981) Shape representation in parallel systems - Hinton, G.E. (1981) Optimizing Neural Networks that Generate Images - Tijmen Tieleman’s disseration 其他论文 Capsule Network Performance on Complex Data - Xi, E., Bing, S. and Jin, Y. (2017) Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture - Qiao, K., Zhang, C., Wang, L., Yan, B., Chen, J., Zeng, L. and Tong, L., (2018) An Optimization View on Dynamic Routing Between Capsules - Wang, D., Liu, E. (2018) CapsuleGAN: Generative Adversarial Capsule Network Ayush Jaiswal, Wael AbdAlmageed, Premkumar Natarajan. (2018) Spectral Capsule Networks - Bahadori, M. T. (2018) 博客 Max Pechyonkin的胶囊网络入门系列： 胶囊网络背后的直觉 胶囊如何工作 囊间动态路由算法 胶囊网络架构 基于TensorFlow实现胶囊网络 Debarko De的胶囊网络教程，包括注释详尽的胶囊网络实现代码 基于CUDA为胶囊网络实现TensorFlow定制操作 Jos van de Wolfshaar的文章，定制胶囊网络运算的CUDA支持 ISI新研究：胶囊生成对抗网络 用胶囊网络替换CNN作为GAN的判别网络，在MNIST数据集上取得了比卷积GAN更好的表现 Uncovering the Intuition behind Capsule Networks and Inverse Graphic Tanay Kothari的长篇教程 A Visual Representation of Capsule Connections in Dynamic Routing Between Capsules Mike Ross的胶囊网络示意图 Capsule Networks Are Shaking up AI — Here’s How to Use Them Nick Bourdakos的介绍 Capsule Networks Explained Kendrick Tan的解释 Understanding Dynamic Routing between Capsules (Capsule Networks) Jonathan Hui的教程，包括注释详尽的基于Keras的胶囊网络实现代码 Matrix capsules with EM routing Adrian Colyer关于EM路由的文章 Capsule Networks: A Glossary Sebastian Kwiatkowski的胶囊网络术语表 Overview of awesome articles 点评三篇胶囊网络教程 视频 Geoffrey Hinton’s talk: What is wrong with convolutional neural nets? - Geoffrey Hinton在MIT. Brain & Cognitive Sciences的演讲《卷积神经网络的问题在哪里？》 Capsule Networks (CapsNets) – Tutorial - “这视频棒极了。我本希望我能把胶囊解释得这么清楚。”Geoffrey Hinton Capsule networks: overview - 胶囊网络概览，包括向量和矩阵胶囊。 Overview of awesome videos 对以上3个视频的点评。 Capsule Networks: An Improvement to Convolutional Networks Siraj Raval介绍胶囊网络的视频 动态路由实现 官方实现 Sarasra/models 《Dynamic Routing Between Capsules》论文所用的代码 TensorFlow alisure-ml/CapsNet bourdakos1/capsule-networks etendue/CapsNet_TF InnerPeace-Wu/CapsNet-tensorflow jaesik817/adv_attack_capsnet jostosh/capsnet JunYeopLee/capsule-networks laodar/tf_CapsNet leoniloris/CapsNet naturomics/CapsNet-Tensorflow rrqq/CapsNet-tensorflow-jupyter thibo73800/capsnet-traffic-sign-classifier tjiang31/CapsNet winwinJJiang/capsNet-Tensorflow PyTorch acburigo/CapsNet adambielski/CapsNet-pytorch AlexHex7/CapsNet_pytorch aliasvishnu/Capsule-Networks-Notebook-MNIST andreaazzini/capsnet.pytorch cedrickchee/capsule-net-pytorch dragen1860/CapsNet-Pytorch gram-ai/capsule-networks higgsfield/Capsule-Network-Tutorial laubonghaudoi/CapsNet_guide_PyTorch leftthomas/CapsNet nishnik/CapsNet-PyTorch tonysy/CapsuleNet-PyTorch Ujjwal-9/CapsNet Keras fengwang/minimal-capsule gusgad/capsule-GAN mitiku1/Emopy-CapsNet ruslangrimov/capsnet-with-capsulewise-convolution streamride/CapsNet-keras-imdb sunxirui310/CapsNet-Keras theblackcat102/dynamic-routing-capsule-cifar XifengGuo/CapsNet-Keras XifengGuo/CapsNet-Fashion-MNIST Chainer soskek/dynamic_routing_between_capsules Torch mrkulk/Unsupervised-Capsule-Network MXNet AaronLeong/CapsNet_Mxnet GarrickLin/Capsnet.Gluon Soonhwan-Kwon/capsnet.mxnet CNTK Southworkscom/CapsNet-CNTK Lasagne DeniskaMazur/CapsNet-Lasagne Matlab yechengxi/LightCapsNet R dfalbel/capsnet JavaScript alseambusher/capsnet.js Vulcan moothyknight/CapsNet-for-Graphics-Rendering-Optimization EM路由实现 TensorFlow gyang274/capsulesEM www0wwwjs1/Matrix-Capsules-EM-Tensorflow PyTorch shzygmyx/Matrix-Capsules-pytorch 其他资源 Capsule Networks discussion Facebook讨论组 CapsNet-Tensorflow CapsNet-Tensorflow的gitter.im讨论组 Will capsule networks replace neural networks? Quora问答“胶囊网络会取代神经网络吗？” Could GANs work with Hinton’s capsule theory? Quora问答“GAN可以应用Hinton的胶囊理论吗？” Dynamic Routing Between Capsules Kyuhwan Jung对论文《Dynamic routing between Capsules》的评论（slideshare）

Based on Pytorch, the Adversarial Attack algorithm DeepFool, targeting the Mnist data set and ResNet18 network

Adversarial attack on a CNN trained on MNIST dataset using Targeted I-FGSM and Targeted MI-FGM

Investigating the robustness of various supervised learning models against adversarial attacks on the MNIST dataset. Implements the Fast Gradient Sign Method (FGSM) to generate adversarial examples and evaluates model performance under varying perturbation levels. Includes Jupyter Notebook with code, visualizations, and analysis.

PyTorch implementations of MNIST adversarial attacks, including CW, FGSM, and PGD methods on CNN, MLP, LSTM, and VGG models. Repository includes training scripts, attack generation, and result visualization, with organized structure for easy navigation and experiment replication.

Adversarial Attacks on MNIST

Repository consists of pre-trained CNN model in pytorch, hitting 89% on Fashion MNIST dataset. Adversarial attack was implemented on a given model. Results are below.

WideResNet implementation on MNIST dataset. FGSM and PGD adversarial attacks on standard training, PGD adversarial training, and Feature Scattering adversarial training.

Generating adversarial examples using Generative Adversarial Neural networks (GANs). Performed black box attacks on attacks on Madry lab challenge MNIST, CIFAR-10 models with excellent results and white box attacks on ImageNet Inception V3.

Deep learning project for MNIST digit classification with model training, saliency map visualization, and robustness evaluation using FGSM adversarial attacks.

adversarial-mnist

Fast Gradient Sign Method

Understanding Adversarial Attacks Through MNIST

Prure MNIST images and Adversarially Attacked MNIST images

White box adversarial attack on MNIST

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

adversarial attacks on a simple MNIST neural network

Demonstration and Implementation of Adversarial Attack and Adversarial training based on PGD attack for MNIST using Pytorch

An exploration of adversarial attacks on MNIST and CIFAR10 datasets

In this project, we used Deepfool and FGSM to perform adversarial attacks on the MNIST dataset, reducing image recognition accuracy to 2% and 22%, respectively. Then, we used APE-GAN and the U-net from the diffusion model to clean the images, restoring the model's recognition accuracy to 80% and 96%, respectively.

pgd and trojan horse Attacks on MNIST dataset

Adversarial attack performed on MNIST.

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Reproducing the FGSM adversarial attack on MNIST using PyTorch.

Adversarial attack analysis on MNIST using PGD and CNN models.

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