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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 351–360 of 492 papers

TitleStatusHype
INK: Inheritable Natural Backdoor Attack Against Model Distillation—0
Learning and Unlearning of Fabricated Knowledge in Language Models—0
Learning to Forget using Hypernetworks—0
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning—0
Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest—0
Mendata: A Framework to Purify Manipulated Training Data—0
Mitigating backdoor attacks in LSTM-based Text Classification Systems by Backdoor Keyword Identification—0
Mitigating Data Poisoning in Text Classification with Differential Privacy—0
Mitigating the Impact of Adversarial Attacks in Very Deep Networks—0
Mixed Strategy Game Model Against Data Poisoning Attacks—0
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