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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 101–110 of 492 papers

TitleStatusHype
Data Poisoning in LLMs: Jailbreak-Tuning and Scaling LawsCode3
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense—0
Model Hijacking Attack in Federated Learning—0
Blockchain for Large Language Model Security and Safety: A Holistic Survey—0
Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization—0
Data Poisoning: An Overlooked Threat to Power Grid Resilience—0
Turning Generative Models Degenerate: The Power of Data Poisoning Attacks—0
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor AttacksCode0
Defending Against Repetitive Backdoor Attacks on Semi-supervised Learning through Lens of Rate-Distortion-Perception Trade-offCode0
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning—0
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