SOTAVerified

Data Poisoning

Data Poisoning is an adversarial attack that tries to manipulate the training dataset in order to control the prediction behavior of a trained model such that the model will label malicious examples into a desired classes (e.g., labeling spam e-mails as safe).

Source: Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 321–330 of 492 papers

TitleStatusHype
UTrace: Poisoning Forensics for Private Collaborative Learning—0
VPN: Verification of Poisoning in Neural Networks—0
What's Pulling the Strings? Evaluating Integrity and Attribution in AI Training and Inference through Concept Shift—0
What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners?—0
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning—0
Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoning—0
Wolf in Sheep's Clothing - The Downscaling Attack Against Deep Learning Applications—0
You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion—0
Histopathological Image Classification and Vulnerability Analysis using Federated Learning—0
How Robust are Randomized Smoothing based Defenses to Data Poisoning?—0
Show:102550
← PrevPage 33 of 50Next →

No leaderboard results yet.