Top 39 adversarial-attacks open source projects

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

Data augmentation for NLP

[EMNLP 2020] "T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack" by Boxin Wang, Hengzhi Pei, Boyuan Pan, Qian Chen, Shuohang Wang, Bo Li

Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]

Code for the paper "Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets", ICCV 2019

Paper list of Adversarial Examples

Python tools for analyzing the robustness properties of neural networks (NNs) from MIT ACL

Adversarial Ranking Attack and Defense, ECCV, 2020.

Robust Out-of-distribution Detection in Neural Networks

Code for the AAAI 2022 publication "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"

Source code and dataset for the CCKS201 paper "On Robustness and Bias Analysis of BERT-based Relation Extraction"

Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".

Reimplementing cool papers in PyTorch...

[CVPR 2022] S-attack library. Official implementation of two papers "Vehicle trajectory prediction works, but not everywhere" and "Are socially-aware trajectory prediction models really socially-aware?".

No.2 solution of Tianchi ImageNet Adversarial Attack Challenge.

Adversarial Attacks on Deep Neural Networks for Time Series Classification

This is a collection of algorithms and approaches used in the book adversarial deep learning

"GAN 인 액션"(한빛미디어, 2020)의 코드 저장소입니다.

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-…

Python toolbox to evaluate graph vulnerability and robustness (CIKM 2021)

A pytorch implementation of "Towards Deep Learning Models Resistant to Adversarial Attacks"

Codes for NeurIPS 2020 paper "Adversarial Weight Perturbation Helps Robust Generalization"

[ICCV2021 Oral] Fooling LiDAR by Attacking GPS Trajectory

Task-agnostic universal black-box attacks on computer vision neural network via procedural noise (CCS'19)

TrojanZoo provides a universal pytorch platform to conduct security researches (especially backdoor attacks/defenses) of image classification in deep learning.

CHOP: An optimization library based on PyTorch, with applications to adversarial examples and structured neural network training.

Natural Language Attacks in a Hard Label Black Box Setting.

KitanaQA: Adversarial training and data augmentation for neural question-answering models

Attacking Optical Flow (ICCV 2019)

AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds (ECCV 2020)

An Algorithm to Quantify Robustness of Recurrent Neural Networks

Geometric Adversarial Attacks and Defenses on 3D Point Clouds (3DV 2021)

Code for the unrestricted adversarial examples paper (NeurIPS 2018)

Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

No description or website provided.

Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph Machine Learning.