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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 301–310 of 492 papers

TitleStatusHype
Towards Poisoning Fair Representations—0
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization—0
Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks—0
Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization—0
Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning—0
Training set cleansing of backdoor poisoning by self-supervised representation learning—0
Data Poisoning Attack Aiming the Vulnerability of Continual Learning—0
Model-Agnostic Explanations using Minimal Forcing Subsets—0
TrojanTime: Backdoor Attacks on Time Series Classification—0
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning—0
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