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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 476–492 of 492 papers

TitleStatusHype
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
Hard Work Does Not Always Pay Off: Poisoning Attacks on Neural Architecture Search—0
Have You Poisoned My Data? Defending Neural Networks against Data Poisoning—0
Histopathological Image Classification and Vulnerability Analysis using Federated Learning—0
How Robust are Randomized Smoothing based Defenses to Data Poisoning?—0
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning—0
WW-FL: Secure and Private Large-Scale Federated Learning—0
Hyperparameter Learning under Data Poisoning: Analysis of the Influence of Regularization via Multiobjective Bilevel Optimization—0
If You Don't Understand It, Don't Use It: Eliminating Trojans with Filters Between Layers—0
Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition—0
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving—0
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
Influence Based Defense Against Data Poisoning Attacks in Online Learning—0
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