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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 351–375 of 492 papers

TitleStatusHype
Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario—0
Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks—0
An Investigation of Data Poisoning Defenses for Online Learning—0
An Optimal Control View of Adversarial Machine Learning—0
A Novel Pearson Correlation-Based Merging Algorithm for Robust Distributed Machine Learning with Heterogeneous Data—0
Approaching the Harm of Gradient Attacks While Only Flipping Labels—0
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains—0
A Robust Attack: Displacement Backdoor Attack—0
Provable Training of a ReLU Gate with an Iterative Non-Gradient Algorithm—0
Atlas: A Framework for ML Lifecycle Provenance & Transparency—0
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions—0
Attacks on the neural network and defense methods—0
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning—0
Backdoor Attack and Defense for Deep Regression—0
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation—0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study—0
Certifiers Make Neural Networks Vulnerable to Availability Attacks—0
Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation—0
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers—0
Backdoor Vulnerabilities in Normally Trained Deep Learning Models—0
BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning—0
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Bait and Switch: Online Training Data Poisoning of Autonomous Driving Systems—0
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation—0
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems—0
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