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

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.

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

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

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

💡 Adversarial attacks on model explanations, and evaluation approaches

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"

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

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

Understanding and Improving Fast Adversarial Training [NeurIPS 2020]

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

Certified defense to adversarial examples using CROWN and IBP. Also includes GPU implementation of CROWN verification algorithm (in PyTorch).

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