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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 326–350 of 492 papers

TitleStatusHype
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense—0
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection—0
TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks—0
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks—0
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning—0
ABC-FL: Anomalous and Benign client Classification in Federated Learning—0
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation—0
Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles—0
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners—0
Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems—0
Adversarial Clean Label Backdoor Attacks and Defenses on Text Classification Systems—0
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It—0
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks—0
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era—0
Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors—0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
Adversarial Vulnerability of Active Transfer Learning—0
A Framework of Randomized Selection Based Certified Defenses Against Data Poisoning Attacks—0
A GAN-based data poisoning framework against anomaly detection in vertical federated learning—0
A Geometric Approach to Problems in Optimization and Data Science—0
A Gradient Method for Multilevel Optimization—0
A Linear Approach to Data Poisoning—0
A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters—0
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