adversarial-examples

AdNauseam: Fight back against advertising surveillance

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

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.

A Toolbox for Adversarial Robustness Research

A pytorch adversarial library for attack and defense methods on images and graphs

🗣️ Tool to generate adversarial text examples and test machine learning models against them

Implementation of Papers on Adversarial Examples

Adversarial attacks and defenses on Graph Neural Networks.

A curated list of awesome resources for adversarial examples in deep learning

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models (published in ICLR2018)

DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model

Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural training.

A curated list of papers on adversarial machine learning (adversarial examples and defense methods).

[NeurIPS 2020]auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks

💡 Adversarial attacks on model explanations, and evaluation approaches

Physical adversarial attack for fooling the Faster R-CNN object detector

PyTorch library for adversarial attack and training

[CVPR 2020] When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks

Short PhD seminar on Machine Learning Security (Adversarial Machine Learning)

Code for "Detecting Adversarial Samples from Artifacts" (Feinman et al., 2017)

A PyTorch Toolbox for creating adversarial examples that fool neural networks.

Code for our CVPR 2018 paper, "On the Robustness of Semantic Segmentation Models to Adversarial Attacks"

Understanding and Improving Fast Adversarial Training [NeurIPS 2020]

Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)

alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP'21)

Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs

Code and data of the ACL 2020 paper "Word-level Textual Adversarial Attacking as Combinatorial Optimization"

Patch-wise iterative attack (accepted by ECCV 2020) to improve the transferability of adversarial examples.