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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 171–180 of 492 papers

TitleStatusHype
Blockchain for Large Language Model Security and Safety: A Holistic Survey—0
An Investigation of Data Poisoning Defenses for Online Learning—0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats—0
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation—0
Do-AIQ: A Design-of-Experiment Approach to Quality Evaluation of AI Mislabel Detection Algorithm—0
BrainWash: A Poisoning Attack to Forget in Continual Learning—0
Don't Forget What I did?: Assessing Client Contributions in Federated Learning—0
DP-InstaHide: Data Augmentations Provably Enhance Guarantees Against Dataset Manipulations—0
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm—0
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks—0
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