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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
Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce—0
Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks—0
UTrace: Poisoning Forensics for Private Collaborative Learning—0
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks—0
Clean Label Attacks against SLU Systems—0
Unleashing Worms and Extracting Data: Escalating the Outcome of Attacks against RAG-based Inference in Scale and Severity Using JailbreakingCode0
Context is the Key: Backdoor Attacks for In-Context Learning with Vision Transformers—0
Blockchain-based Federated Recommendation with Incentive Mechanism—0
Protecting against simultaneous data poisoning attacks—0
Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender SystemsCode0
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