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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
Data Poisoning Attacks to Deep Learning Based Recommender Systems—0
Data Poisoning Attacks to Locally Differentially Private Range Query Protocols—0
Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks—0
Data Poisoning Won’t Save You From Facial Recognition—0
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses—0
Data Shifts Hurt CoT: A Theoretical Study—0
Data Taggants: Dataset Ownership Verification via Harmless Targeted Data Poisoning—0
Deep Learning Model Security: Threats and Defenses—0
Deep Probabilistic Models to Detect Data Poisoning Attacks—0
Defend Data Poisoning Attacks on Voice Authentication—0
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