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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 171–180 of 492 papers

TitleStatusHype
Data Poisoning: An Overlooked Threat to Power Grid Resilience—0
Data Poisoning against Differentially-Private Learners: Attacks and Defenses—0
Adversarial Vulnerability of Active Transfer Learning—0
Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study—0
Data-Dependent Stability Analysis of Adversarial Training—0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study—0
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey—0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation—0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
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