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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 211–220 of 492 papers

TitleStatusHype
Have You Poisoned My Data? Defending Neural Networks against Data Poisoning—0
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence RatesCode0
Poisoning Programs by Un-Repairing Code: Security Concerns of AI-generated Code—0
Don't Forget What I did?: Assessing Client Contributions in Federated Learning—0
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer NetworksCode0
Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models—0
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
Purifying Large Language Models by Ensembling a Small Language Model—0
SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms—0
Review-Incorporated Model-Agnostic Profile Injection Attacks on Recommender Systems—0
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