Data Poisoning
38 papers with code • 0 benchmarks • 0 datasets
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
Benchmarks
Datasets
Greatest papers with code
Backdoor Learning: A Survey
Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs), such that the attacked model performs well on benign samples, whereas its prediction will be maliciously changed if the hidden backdoor is activated by the attacker-defined trigger.
How To Backdoor Federated Learning
ebagdasa/backdoor_federated_learning
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An attacker selected in a single round of federated learning can cause the global model to immediately reach 100% accuracy on the backdoor task.
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
We utilize trusted data by proposing a loss correction technique that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers.
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks
aks2203/poisoning-benchmark
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Data poisoning and backdoor attacks manipulate training data in order to cause models to fail during inference.
A Distributed Trust Framework for Privacy-Preserving Machine Learning
Privacy-preserving techniques distribute computation in order to ensure that data remains in the control of the owner while learning takes place.
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
ashafahi/inceptionv3-transferLearn-poison
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NeurIPS 2018
The proposed attacks use "clean-labels"; they don't require the attacker to have any control over the labeling of training data.
Data Poisoning Attacks Against Federated Learning Systems
Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep neural networks in which participants' data remains on their own devices with only model updates being shared with a central server.
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching
JonasGeiping/poisoning-gradient-matching
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ICLR 2021
We consider a particularly malicious poisoning attack that is both "from scratch" and "clean label", meaning we analyze an attack that successfully works against new, randomly initialized models, and is nearly imperceptible to humans, all while perturbing only a small fraction of the training data.
MetaPoison: Practical General-purpose Clean-label Data Poisoning
JonasGeiping/poisoning-gradient-matching
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NeurIPS 2020
Existing attacks for data poisoning neural networks have relied on hand-crafted heuristics, because solving the poisoning problem directly via bilevel optimization is generally thought of as intractable for deep models.
Radioactive data: tracing through training
facebookresearch/radioactive_data
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ICML 2020
The mark is robust to strong variations such as different architectures or optimization methods.
