7,526 research outputs found
Spectral Signatures in Backdoor Attacks
A recent line of work has uncovered a new form of data poisoning: so-called
\emph{backdoor} attacks. These attacks are particularly dangerous because they
do not affect a network's behavior on typical, benign data. Rather, the network
only deviates from its expected output when triggered by a perturbation planted
by an adversary.
In this paper, we identify a new property of all known backdoor attacks,
which we call \emph{spectral signatures}. This property allows us to utilize
tools from robust statistics to thwart the attacks. We demonstrate the efficacy
of these signatures in detecting and removing poisoned examples on real image
sets and state of the art neural network architectures. We believe that
understanding spectral signatures is a crucial first step towards designing ML
systems secure against such backdoor attacksComment: 16 pages, accepted to NIPS 201
Deciding How to Decide: Dynamic Routing in Artificial Neural Networks
We propose and systematically evaluate three strategies for training
dynamically-routed artificial neural networks: graphs of learned
transformations through which different input signals may take different paths.
Though some approaches have advantages over others, the resulting networks are
often qualitatively similar. We find that, in dynamically-routed networks
trained to classify images, layers and branches become specialized to process
distinct categories of images. Additionally, given a fixed computational
budget, dynamically-routed networks tend to perform better than comparable
statically-routed networks.Comment: ICML 2017. Code at https://github.com/MasonMcGill/multipath-nn Video
abstract at https://youtu.be/NHQsDaycwy
A toolbox for animal call recognition
Monitoring the natural environment is increasingly important as habit degradation and climate change reduce theworld’s biodiversity.We have developed software tools and applications to assist ecologists with the collection and analysis of acoustic data at large spatial and temporal scales.One of our key objectives is automated animal call recognition, and our approach has three novel attributes. First, we work with raw environmental audio, contaminated by noise and artefacts and containing calls that vary greatly in volume depending on the animal’s proximity to the microphone. Second, initial experimentation suggested that no single recognizer could dealwith the enormous variety of calls. Therefore, we developed a toolbox of generic recognizers to extract invariant features for each call type. Third, many species are cryptic and offer little data with which to train a recognizer. Many popular machine learning methods require large volumes of training and validation data and considerable time and expertise to prepare. Consequently we adopt bootstrap techniques that can be initiated with little data and refined subsequently. In this paper, we describe our recognition tools and present results for real ecological problems
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