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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 271–280 of 492 papers

TitleStatusHype
Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models—0
Differentially-Private Decision Trees and Provable Robustness to Data PoisoningCode0
From Shortcuts to Triggers: Backdoor Defense with Denoised PoECode0
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate ModelsCode0
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation—0
Evaluating Impact of User-Cluster Targeted Attacks in Matrix Factorisation Recommenders—0
Pick your Poison: Undetectability versus Robustness in Data Poisoning Attacks—0
Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps—0
Interactive System-wise Anomaly Detection—0
INK: Inheritable Natural Backdoor Attack Against Model Distillation—0
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