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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

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Showing 211–220 of 492 papers

TitleStatusHype
Filter, Obstruct and Dilute: Defending Against Backdoor Attacks on Semi-Supervised Learning—0
FLock: Defending Malicious Behaviors in Federated Learning with Blockchain—0
Deep Probabilistic Models to Detect Data Poisoning Attacks—0
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification—0
Deep Learning Model Security: Threats and Defenses—0
Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks—0
Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution—0
Fragile Giants: Understanding the Susceptibility of Models to Subpopulation Attacks—0
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation—0
Data Taggants: Dataset Ownership Verification via Harmless Targeted Data Poisoning—0
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