117 research outputs found
The Adversarial Attack and Detection under the Fisher Information Metric
Many deep learning models are vulnerable to the adversarial attack, i.e.,
imperceptible but intentionally-designed perturbations to the input can cause
incorrect output of the networks. In this paper, using information geometry, we
provide a reasonable explanation for the vulnerability of deep learning models.
By considering the data space as a non-linear space with the Fisher information
metric induced from a neural network, we first propose an adversarial attack
algorithm termed one-step spectral attack (OSSA). The method is described by a
constrained quadratic form of the Fisher information matrix, where the optimal
adversarial perturbation is given by the first eigenvector, and the model
vulnerability is reflected by the eigenvalues. The larger an eigenvalue is, the
more vulnerable the model is to be attacked by the corresponding eigenvector.
Taking advantage of the property, we also propose an adversarial detection
method with the eigenvalues serving as characteristics. Both our attack and
detection algorithms are numerically optimized to work efficiently on large
datasets. Our evaluations show superior performance compared with other
methods, implying that the Fisher information is a promising approach to
investigate the adversarial attacks and defenses.Comment: Accepted as an AAAI-2019 oral pape
Fast and Robust Rank Aggregation against Model Misspecification
In rank aggregation, preferences from different users are summarized into a
total order under the homogeneous data assumption. Thus, model misspecification
arises and rank aggregation methods take some noise models into account.
However, they all rely on certain noise model assumptions and cannot handle
agnostic noises in the real world. In this paper, we propose CoarsenRank, which
rectifies the underlying data distribution directly and aligns it to the
homogeneous data assumption without involving any noise model. To this end, we
define a neighborhood of the data distribution over which Bayesian inference of
CoarsenRank is performed, and therefore the resultant posterior enjoys
robustness against model misspecification. Further, we derive a tractable
closed-form solution for CoarsenRank making it computationally efficient.
Experiments on real-world datasets show that CoarsenRank is fast and robust,
achieving consistent improvement over baseline methods
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