SOTAVerified

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 271–280 of 492 papers

TitleStatusHype
SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization—0
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning—0
Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification—0
Securing Traffic Sign Recognition Systems in Autonomous Vehicles—0
Security and Privacy Challenges in Deep Learning Models—0
Security and Privacy Challenges of Large Language Models: A Survey—0
Security Concerns for Large Language Models: A Survey—0
Security of Distributed Machine Learning: A Game-Theoretic Approach to Design Secure DSVM—0
SEEP: Training Dynamics Grounds Latent Representation Search for Mitigating Backdoor Poisoning Attacks—0
Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks—0
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