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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–150 of 492 papers

TitleStatusHype
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era—0
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection—0
Model Hijacking Attack in Federated Learning—0
Atlas: A Framework for ML Lifecycle Provenance & Transparency—0
Clean Image May be Dangerous: Data Poisoning Attacks Against Deep Hashing—0
Clean Label Attacks against SLU Systems—0
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks—0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
Computation and Data Efficient Backdoor Attacks—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
ControlNET: A Firewall for RAG-based LLM System—0
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks—0
Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy—0
Concealed Data Poisoning Attacks on NLP Models—0
Cut the Deadwood Out: Post-Training Model Purification with Selective Module Substitution—0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey—0
Data-Dependent Stability Analysis of Adversarial Training—0
Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study—0
Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing—0
Data Poisoning: An Overlooked Threat to Power Grid Resilience—0
Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks—0
Provable Training of a ReLU Gate with an Iterative Non-Gradient Algorithm—0
A Robust Attack: Displacement Backdoor Attack—0
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers—0
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm—0
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm—0
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks—0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats—0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications—0
Cascading Adversarial Bias from Injection to Distillation in Language Models—0
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Balancing Privacy, Robustness, and Efficiency in Machine Learning—0
Approaching the Harm of Gradient Attacks While Only Flipping Labels—0
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Breaking Fair Binary Classification with Optimal Flipping Attacks—0
A Novel Pearson Correlation-Based Merging Algorithm for Robust Distributed Machine Learning with Heterogeneous Data—0
Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models—0
BrainWash: A Poisoning Attack to Forget in Continual Learning—0
An Optimal Control View of Adversarial Machine Learning—0
Adversarial Clean Label Backdoor Attacks and Defenses on Text Classification Systems—0
ABC-FL: Anomalous and Benign client Classification in Federated Learning—0
Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy—0
Blockchain for Large Language Model Security and Safety: A Holistic Survey—0
An Investigation of Data Poisoning Defenses for Online Learning—0
Show:102550
← PrevPage 3 of 10Next →

No leaderboard results yet.