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 201–225 of 492 papers

TitleStatusHype
Fed-Credit: Robust Federated Learning with Credibility Management—0
Generalization under Byzantine & Poisoning Attacks: Tight Stability Bounds in Robust Distributed Learning—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Defending Against Backdoor Attacks Using Ensembles of Weak Learners—0
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
Defending against Backdoor Attack on Deep Neural Networks—0
Federated Unlearning—0
Defend Data Poisoning Attacks on Voice Authentication—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
Deep Probabilistic Models to Detect Data Poisoning Attacks—0
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification—0
Deep Learning Model Security: Threats and Defenses—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
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation—0
Data Taggants: Dataset Ownership Verification via Harmless Targeted Data Poisoning—0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
Collaborative Self Organizing Map with DeepNNs for Fake Task Prevention in Mobile Crowdsensing—0
Bait and Switch: Online Training Data Poisoning of Autonomous Driving Systems—0
Computation and Data Efficient Backdoor Attacks—0
A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters—0
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