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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 431–440 of 492 papers

TitleStatusHype
Defending against Backdoor Attack on Deep Neural Networks—0
Defending Against Backdoor Attacks Using Ensembles of Weak Learners—0
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm—0
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks—0
Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy—0
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm—0
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks—0
Detecting Backdoors in Deep Text Classifiers—0
Detection of Physiological Data Tampering Attacks with Quantum Machine Learning—0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications—0
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