Indirect Invisible Poisoning Attacks on Domain Adaptation
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Updated
Jul 12, 2021 - Python
Indirect Invisible Poisoning Attacks on Domain Adaptation
Tensorflow implementation of APT (Fight Fire with Fire: Towards Robust Recommender Systems via Adversarial Poisoning Training. SIGIR 2021)
Tensorflow implementation of TrialAttack (Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems. KDD 2021)
Tensorflow implementation of TrialAttack (Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems. KDD 2021)
Tensorflow implementation of TrialAttack (Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems. KDD 2021)
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
Hack tool for local network: Man in the middle, hosts scan, ARP poisoning, Router and DNS Poisoning
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.
Continuous Integration And Continuous Delivery Poisoning Guides
Poisoning attack methods against adversarial training algorithms
Taller de Adversarial Machine Learning
PhD/MSc course on Machine Learning Security (Univ. Cagliari)
A Survey of Poisoning Attacks and Defenses in Recommender Systems
Test tool to simulate two types of poisoning attack on AI model
Test tool to simulate defense from poisoning attack on AI model
Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
A Python library for Secure and Explainable Machine Learning
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.
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
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