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Found 3 papers, 3 papers with code
Local Intrinsic Dimensionality Signals Adversarial Perturbations
In this paper, we derive a lower-bound and an upper-bound for the LID value of a perturbed data point and demonstrate that the bounds, in particular the lower-bound, has a positive correlation with the magnitude of the perturbation.
Defending Distributed Classifiers Against Data Poisoning Attacks
We introduce a weighted SVM against such attacks using K-LID as a distinguishing characteristic that de-emphasizes the effect of suspicious data samples on the SVM decision boundary.
Defending Regression Learners Against Poisoning Attacks
Regression models, which are widely used from engineering applications to financial forecasting, are vulnerable to targeted malicious attacks such as training data poisoning, through which adversaries can manipulate their predictions.
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