Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Paper summary Ma et al. detect adversarial examples based on their estimated intrinsic dimensionality. I want to note that this work is also similar to [1] – in both publications, local intrinsic dimensionality is used to analyze adversarial examples. Specifically, the intrinsic dimensionality of a sample is estimated based on the radii $r_i(x)$ of the $k$-nearest neighbors around a sample $x$: $- \left(\frac{1}{k} \sum_{i = 1}^k \log \frac{r_i(x)}{r_k(x)}\right)^{-1}$. For details regarding the original, theoretical formulation of local intrinsic dimensionality I refer to the paper. In experiments, the authors show that adversarial examples exhibit a significant higher intrinsic dimensionality than training samples or randomly perturbed examples. This observation allows detection of adversarial examples. A proper interpretation of this finding is, however, missing. It would be interesting to investigate what this finding implies about the properties of adversarial examples.
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Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Xingjun Ma and Bo Li and Yisen Wang and Sarah M. Erfani and Sudanthi Wijewickrema and Grant Schoenebeck and Dawn Song and Michael E. Houle and James Bailey
arXiv e-Print archive - 2018 via Local arXiv
Keywords: cs.LG, cs.CR, cs.CV

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Summary by David Stutz 1 month ago
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