SOTAVerified

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 451–475 of 492 papers

TitleStatusHype
Evaluating Impact of User-Cluster Targeted Attacks in Matrix Factorisation Recommenders—0
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning—0
Explainable Label-flipping Attacks on Human Emotion Assessment System—0
Exploring Vulnerabilities and Protections in Large Language Models: A Survey—0
Face Recognition in the age of CLIP & Billion image datasets—0
Fairness-aware Summarization for Justified Decision-Making—0
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks—0
FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning—0
Fed-Credit: Robust Federated Learning with Credibility Management—0
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy—0
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application—0
Federated Unlearning—0
FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in FL—0
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users—0
Filter, Obstruct and Dilute: Defending Against Backdoor Attacks on Semi-Supervised Learning—0
FLock: Defending Malicious Behaviors in Federated Learning with Blockchain—0
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification—0
Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning 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
FR-GAN: Fair and Robust Training—0
Generalization under Byzantine & Poisoning Attacks: Tight Stability Bounds in Robust Distributed Learning—0
Generating Fake Cyber Threat Intelligence Using Transformer-Based Models—0
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