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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 191–200 of 492 papers

TitleStatusHype
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
Collaborative Self Organizing Map with DeepNNs for Fake Task Prevention in Mobile Crowdsensing—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
Attacks on the neural network and defense methods—0
Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors—0
Clean Label Attacks against SLU Systems—0
Clean Image May be Dangerous: Data Poisoning Attacks Against Deep Hashing—0
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning—0
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions—0
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era—0
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