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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 201–225 of 492 papers

TitleStatusHype
FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in FL—0
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application—0
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning—0
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions—0
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks—0
Federated Unlearning—0
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy—0
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users—0
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning—0
FLock: Defending Malicious Behaviors in Federated Learning with Blockchain—0
Fed-Credit: Robust Federated Learning with Credibility Management—0
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification—0
FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning—0
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks—0
Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution—0
Fragile Giants: Understanding the Susceptibility of Models to Subpopulation Attacks—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
Atlas: A Framework for ML Lifecycle Provenance & Transparency—0
Fairness-aware Summarization for Justified Decision-Making—0
Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing—0
Face Recognition in the age of CLIP & Billion image datasets—0
Exploring Vulnerabilities and Protections in Large Language Models: A Survey—0
Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks—0
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