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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 451–492 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
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Get a Model! Model Hijacking Attack Against Machine Learning Models—0
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV—0
GFL: A Decentralized Federated Learning Framework Based On Blockchain—0
Gradient-based Data Subversion Attack Against Binary Classifiers—0
Hard Work Does Not Always Pay Off: Poisoning Attacks on Neural Architecture Search—0
Have You Poisoned My Data? Defending Neural Networks against Data Poisoning—0
Histopathological Image Classification and Vulnerability Analysis using Federated Learning—0
How Robust are Randomized Smoothing based Defenses to Data Poisoning?—0
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning—0
WW-FL: Secure and Private Large-Scale Federated Learning—0
Hyperparameter Learning under Data Poisoning: Analysis of the Influence of Regularization via Multiobjective Bilevel Optimization—0
If You Don't Understand It, Don't Use It: Eliminating Trojans with Filters Between Layers—0
Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition—0
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving—0
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
Influence Based Defense Against Data Poisoning Attacks in Online Learning—0
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