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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 51–60 of 492 papers

TitleStatusHype
DROP: Poison Dilution via Knowledge Distillation for Federated LearningCode0
Filter, Obstruct and Dilute: Defending Against Backdoor Attacks on Semi-Supervised Learning—0
Detection of Physiological Data Tampering Attacks with Quantum Machine Learning—0
SoK: Benchmarking Poisoning Attacks and Defenses in Federated LearningCode2
Safety at Scale: A Comprehensive Survey of Large Model SafetyCode3
TrojanTime: Backdoor Attacks on Time Series Classification—0
Provably effective detection of effective data poisoning attacks—0
Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender SystemsCode0
A Novel Pearson Correlation-Based Merging Algorithm for Robust Distributed Machine Learning with Heterogeneous Data—0
Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution—0
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