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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 371–380 of 492 papers

TitleStatusHype
BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning—0
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Bait and Switch: Online Training Data Poisoning of Autonomous Driving Systems—0
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation—0
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems—0
Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps—0
BiCert: A Bilinear Mixed Integer Programming Formulation for Precise Certified Bounds Against Data Poisoning Attacks—0
Blockchain-based Federated Recommendation with Incentive Mechanism—0
Blockchain for Large Language Model Security and Safety: A Holistic Survey—0
Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy—0
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