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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 471–480 of 492 papers

TitleStatusHype
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
FR-GAN: Fair and Robust Training—0
Generalization under Byzantine & Poisoning Attacks: Tight Stability Bounds in Robust Distributed Learning—0
Generating Fake Cyber Threat Intelligence Using Transformer-Based Models—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Get a Model! Model Hijacking Attack Against Machine Learning Models—0
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV—0
GFL: A Decentralized Federated Learning Framework Based On Blockchain—0
Gradient-based Data Subversion Attack Against Binary Classifiers—0
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