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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 161–170 of 492 papers

TitleStatusHype
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems—0
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning—0
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning—0
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks—0
Influence-Driven Data Poisoning in Graph-Based Semi-Supervised Classifiers—0
Detecting Backdoors in Deep Text Classifiers—0
Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models—0
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners—0
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm—0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats—0
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