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Adversarial attacks by malicious actors on machine learning systems, such as introducing poison triggers into training datasets, pose significant risks.
Learning multiple layers of features from tiny images
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Deep residual learning for image recognition
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Detection of adversarial training examples in poisoning attacks through anomaly detection
Andrea Paudice, Luis Muñoz-González, Andras Gyorgy, and Emil C Lupu · 2018
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A new backdoor attack in cnns by training set corruption without label poisoning
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Badnets: Evaluating backdooring attacks on deep neural networks
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
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Anti-backdoor learning: Training clean models on poisoned data
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A law of adversarial risk, interpolation, and label noise, 2023
Daniel Paleka and Amartya Sanyal · 2023
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Poisoning web-scale training datasets is practical, 2024
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Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, and Xingjun Ma · 2021
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Backdoor attacks on spiking nns and neuromorphic datasets
Gorka Abad, Oguzhan Ersoy, Stjepan Picek, Víctor Julio Ramírez-Durán, and Aitor Urbieta · 2022
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Fast machine unlearning without retraining through selective synaptic dampening
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Jack Foster, Stefan Schoepf, and Alexandra Brintrup
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