2,508 research outputs found
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction
Deep neural networks are vulnerable to adversarial examples, i.e.,
carefully-perturbed inputs aimed to mislead classification. This work proposes
a detection method based on combining non-linear dimensionality reduction and
density estimation techniques. Our empirical findings show that the proposed
approach is able to effectively detect adversarial examples crafted by
non-adaptive attackers, i.e., not specifically tuned to bypass the detection
method. Given our promising results, we plan to extend our analysis to adaptive
attackers in future work.Comment: European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN) 201
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features
One-class support vector machine (OC-SVM) for a long time has been one of the
most effective anomaly detection methods and extensively adopted in both
research as well as industrial applications. The biggest issue for OC-SVM is
yet the capability to operate with large and high-dimensional datasets due to
optimization complexity. Those problems might be mitigated via dimensionality
reduction techniques such as manifold learning or autoencoder. However,
previous work often treats representation learning and anomaly prediction
separately. In this paper, we propose autoencoder based one-class support
vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier
features to approximate the radial basis kernel, into deep learning context by
combining it with a representation learning architecture and jointly exploit
stochastic gradient descent to obtain end-to-end training. Interestingly, this
also opens up the possible use of gradient-based attribution methods to explain
the decision making for anomaly detection, which has ever been challenging as a
result of the implicit mappings between the input space and the kernel space.
To the best of our knowledge, this is the first work to study the
interpretability of deep learning in anomaly detection. We evaluate our method
on a wide range of unsupervised anomaly detection tasks in which our end-to-end
training architecture achieves a performance significantly better than the
previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201
Adversarial attacks hidden in plain sight
Convolutional neural networks have been used to achieve a string of successes
during recent years, but their lack of interpretability remains a serious
issue. Adversarial examples are designed to deliberately fool neural networks
into making any desired incorrect classification, potentially with very high
certainty. Several defensive approaches increase robustness against adversarial
attacks, demanding attacks of greater magnitude, which lead to visible
artifacts. By considering human visual perception, we compose a technique that
allows to hide such adversarial attacks in regions of high complexity, such
that they are imperceptible even to an astute observer. We carry out a user
study on classifying adversarially modified images to validate the perceptual
quality of our approach and find significant evidence for its concealment with
regards to human visual perception
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
- …