poisoning-attacks

There are 24 repositories under poisoning-attacks topic.

  • AIJack

    Koukyosyumei/AIJack

    Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)

    Language:C++37444760

  • unica-mlsec/mlsec

    PhD/MSc course on Machine Learning Security (Univ. Cagliari)

    Language:Jupyter Notebook20211023

  • pralab/secml

    A Python library for Secure and Explainable Machine Learning

    Language:Jupyter Notebook16051725

  • reds-lab/Narcissus

    The official implementation of the CCS'23 paper, Narcissus clean-label backdoor attack -- only takes THREE images to poison a face recognition dataset in a clean-label way and achieves a 99.89% attack success rate.

    Language:Python10521012

  • GillHuang-Xtler/flPapers

    Paper collection of federated learning. Conferences and Journals Collection for Federated Learning from 2019 to 2021, Accepted Papers, Hot topics and good research groups. Paper summary

    963124

  • tamlhp/awesome-recsys-poisoning

    A Survey of Poisoning Attacks and Defenses in Recommender Systems

    29233

  • MadryLab/rethinking-backdoor-attacks

    Language:Python1630

  • Daftstone/TrialAttack

    Tensorflow implementation of TrialAttack (Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems. KDD 2021)

    Language:Python11200

  • jiep/adversarial-machine-learning

    Taller de Adversarial Machine Learning

    Language:Jupyter Notebook10320

  • Daftstone/APT

    Tensorflow implementation of APT (Fight Fire with Fire: Towards Robust Recommender Systems via Adversarial Poisoning Training. SIGIR 2021)

    Language:Python8210
  • FedAnil

    rezafotohi/FedAnil

    FedAnil is a secure blockchain-enabled Federated Deep Learning Model to address non-IID data and privacy concerns. This repo hosts a simulation for FedAnil written in Python.

    Language:Python6101

  • xaviermonin/ControlTower

    Hack tool for local network: Man in the middle, hosts scan, ARP poisoning, Router and DNS Poisoning

    Language:C#6210

  • rezafotohi/FedAnilPlus

    FedAnil+ is a novel lightweight, and secure Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil+ written in Python.

    Language:Python4111

  • rezafotohi/FedAnilPlusPlus

    FedAnil++ is a Privacy-Preserving and Communication-Efficient Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil++ written in Python.

    Language:Python4100

  • zjfheart/Poison-adv-training

    Poisoning attack methods against adversarial training algorithms

    Language:Python3100

  • junwu6/I2Attack

    Indirect Invisible Poisoning Attacks on Domain Adaptation

    Language:Python2100

  • dahmansphi/attackai

    Test tool to simulate two types of poisoning attack on AI model

    Language:Python1200

  • dahmansphi/protectai

    Test tool to simulate defense from poisoning attack on AI model

    Language:Python1200

  • theaqueen21/CI-CD-Pipeline-Poisoning

    Continuous Integration And Continuous Delivery Poisoning Guides

    1101

  • USTCLLM/TrialAttack

    Tensorflow implementation of TrialAttack (Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems. KDD 2021)

    Language:Python11

  • awesome-recsys-poisoning/awesome-recsys-poisoning.github.io

    Official Website of https://github.com/tamlhp/awesome-recsys-poisoning

    Language:HTML0100

  • GadigeSrinivas/Identification-of-poisonous-and-non-poisonous-plants

    This project uses Python and machine learning to classify plant species as poisonous or non-poisonous. It aims to provide an efficient way to identify safe and harmful plants, useful for botanists, hikers, and the agricultural sector.

    Language:Python0200

  • SESARLab/ensemble-random-forest-robustness-against-poisoning

    M. Anisetti, C. A. Ardagna, A. Balestrucci, N. Bena, E. Damiani, C. Y. Yeun. "On the Robustness of Random Forest Against Data Poisoning: An Ensemble-Based Approach". In IEEE TSUSC, vol. 8 no. 4

    30

  • USTCLLM/APT

    Tensorflow implementation of TrialAttack (Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems. KDD 2021)

    Language:Python00