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mnist_challenge

A challenge to explore adversarial robustness of neural networks on MNIST.

models

Pre-trained and Reproduced Deep Learning Models （经典复现模型）

models-1

Models and examples built with TensorFlow

nettack

Implementation of the paper "Adversarial Attacks on Neural Networks for Graph Data".

nlp-tutorial

Natural Language Processing Tutorial for Deep Learning Researchers

nlp_adversarial_examples

Implementation code for the paper "Generating Natural Language Adversarial Examples"

nn_robust_attacks

Robust evasion attacks against neural network to find adversarial examples

nndl.github.io

《神经网络与深度学习》 Neural Network and Deep Learning

node_embedding_attack

Adversarial Attacks on Node Embeddings via Graph Poisoning

offer

程序员面试金典 中面试考题的代码实现

one-pixel-attack-keras

Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

projected_sinkhorn

stadv

Spatially Transformed Adversarial Examples with TensorFlow

taadpapers

Must-read Papers on Textual Adversarial Attack and Defense

tensorflow-adversarial

Crafting adversarial images

text_claasification

text_gcn

Graph Convolutional Networks for Text Classification. AAAI 2019

textfool

Plausible looking adversarial examples for text classification

tfword2vec

torchtext-summary

torchtext使用总结，从零开始逐步实现了torchtext文本预处理过程，包括截断补长，词表构建，使用预训练词向量，构建可用于PyTorch的可迭代数据等步骤。并结合Pytorch实现LSTM.