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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 361–370 of 492 papers

TitleStatusHype
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions—0
Attacks on the neural network and defense methods—0
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning—0
Backdoor Attack and Defense for Deep Regression—0
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation—0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study—0
Certifiers Make Neural Networks Vulnerable to Availability Attacks—0
Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation—0
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers—0
Backdoor Vulnerabilities in Normally Trained Deep Learning Models—0
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