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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 391–400 of 492 papers

TitleStatusHype
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning—0
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning—0
Clean Image May be Dangerous: Data Poisoning Attacks Against Deep Hashing—0
Clean Label Attacks against SLU Systems—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
Collaborative Self Organizing Map with DeepNNs for Fake Task Prevention in Mobile Crowdsensing—0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
Computation and Data Efficient Backdoor Attacks—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
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