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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 171–180 of 492 papers

TitleStatusHype
Unlearnable Examples Detection via Iterative Filtering—0
Sonic: Fast and Transferable Data Poisoning on Clustering Algorithms—0
2D-OOB: Attributing Data Contribution Through Joint Valuation FrameworkCode0
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense—0
Model Hijacking Attack in Federated Learning—0
Blockchain for Large Language Model Security and Safety: A Holistic Survey—0
Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization—0
Data Poisoning: An Overlooked Threat to Power Grid Resilience—0
Turning Generative Models Degenerate: The Power of Data Poisoning Attacks—0
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor AttacksCode0
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