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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 451–460 of 492 papers

TitleStatusHype
Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest—0
FR-GAN: Fair and Robust Training—0
Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing—0
Detection of Backdoors in Trained Classifiers Without Access to the Training Set—0
On Defending Against Label Flipping Attacks on Malware Detection Systems—0
Seeing is Not Believing: Camouflage Attacks on Image Scaling AlgorithmsCode0
Explaining Vulnerabilities to Adversarial Machine Learning through Visual AnalyticsCode0
Poisoning Attacks with Generative Adversarial NetsCode0
Mixed Strategy Game Model Against Data Poisoning Attacks—0
An Investigation of Data Poisoning Defenses for Online Learning—0
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