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 291–300 of 492 papers

TitleStatusHype
Efficient Reward Poisoning Attacks on Online Deep Reinforcement LearningCode0
BagFlip: A Certified Defense against Data PoisoningCode0
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning—0
PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
VPN: Verification of Poisoning in Neural Networks—0
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning—0
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV—0
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy—0
Indiscriminate Data Poisoning Attacks on Neural NetworksCode0
Show:102550
← PrevPage 30 of 50Next →

No leaderboard results yet.