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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 421–430 of 492 papers

TitleStatusHype
Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender SystemsCode0
Poisoning Attacks with Generative Adversarial NetsCode0
Defending Against Disinformation Attacks in Open-Domain Question AnsweringCode0
Differentially-Private Decision Trees and Provable Robustness to Data PoisoningCode0
From Shortcuts to Triggers: Backdoor Defense with Denoised PoECode0
Unleashing Worms and Extracting Data: Escalating the Outcome of Attacks against RAG-based Inference in Scale and Severity Using JailbreakingCode0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
Detecting AI Trojans Using Meta Neural AnalysisCode0
Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender SystemsCode0
Robust Yet Efficient Conformal Prediction SetsCode0
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