[NeurIPS 2022] Can Adversarial Training Be Manipulated By Non-Robust Features?

A repository for the experimental framework for in-stream data poisoning monitoring.

Code for the paper Analysis and Detectability of Offline Data Poisoning Attacks on Linear Systems.

MIT IEEE URTC 2023. GSET 2023. Repository for "SeBRUS: Mitigating Data Poisoning in Crowdsourced Datasets with Blockchain". Using Ethereum smart contracts to stop AI security attacks on crowdsourced datasets.

A backdoor attack in a Federated learning setting using the FATE framework

CCS'22 Paper: "Identifying a Training-Set Attack’s Target Using Renormalized Influence Estimation"

Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. (Neurips 2021)

[ICLR 2023, Spotlight] Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning

Analyzing Adversarial Bias and the Robustness of Fair Machine Learning

How Robust are Randomized Smoothing based Defenses to Data Poisoning? (CVPR 2021)

APBench: A Unified Availability Poisoning Attack and Defenses Benchmark (TMLR 08/2024)

Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression

[NeurIPS 2021] Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training

🤯 AI Security EXPOSED! Live Demos Showing Hidden Risks of 🤖 Agentic AI Flows: 💉Prompt Injection, ☣️ Data Poisoning. Watch the recorded session:

Measure and Boost Backdoor Robustness

The official implementation of USENIX Security'23 paper "Meta-Sift" -- Ten minutes or less to find a 1000-size or larger clean subset on poisoned dataset.

Experiments on Data Poisoning Regression Learning

A curated list of academic events on AI Security & Privacy

A curated list of papers & resources linked to data poisoning, backdoor attacks and defenses against them (no longer maintained)