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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 181–190 of 492 papers

TitleStatusHype
BrainWash: A Poisoning Attack to Forget in Continual Learning—0
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems—0
PACOL: Poisoning Attacks Against Continual Learners—0
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models—0
From Trojan Horses to Castle Walls: Unveiling Bilateral Data Poisoning Effects in Diffusion ModelsCode0
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification—0
Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models—0
PrivacyGAN: robust generative image privacy—0
Histopathological Image Classification and Vulnerability Analysis using Federated Learning—0
Transferable Availability Poisoning AttacksCode0
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