8,464 research outputs found
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization
Solving for adversarial examples with projected gradient descent has been
demonstrated to be highly effective in fooling the neural network based
classifiers. However, in the black-box setting, the attacker is limited only to
the query access to the network and solving for a successful adversarial
example becomes much more difficult. To this end, recent methods aim at
estimating the true gradient signal based on the input queries but at the cost
of excessive queries. We propose an efficient discrete surrogate to the
optimization problem which does not require estimating the gradient and
consequently becomes free of the first order update hyperparameters to tune.
Our experiments on Cifar-10 and ImageNet show the state of the art black-box
attack performance with significant reduction in the required queries compared
to a number of recently proposed methods. The source code is available at
https://github.com/snu-mllab/parsimonious-blackbox-attack.Comment: Accepted and to appear at ICML 201
Segregating Event Streams and Noise with a Markov Renewal Process Model
DS and MP are supported by EPSRC Leadership Fellowship EP/G007144/1
Summary Based Structures with Improved Sublinear Recovery for Compressed Sensing
We introduce a new class of measurement matrices for compressed sensing,
using low order summaries over binary sequences of a given length. We prove
recovery guarantees for three reconstruction algorithms using the proposed
measurements, including minimization and two combinatorial methods. In
particular, one of the algorithms recovers -sparse vectors of length in
sublinear time , and requires at most
measurements. The empirical oversampling constant
of the algorithm is significantly better than existing sublinear recovery
algorithms such as Chaining Pursuit and Sudocodes. In particular, for and , the oversampling factor is between 3 to 8. We provide
preliminary insight into how the proposed constructions, and the fast recovery
scheme can be used in a number of practical applications such as market basket
analysis, and real time compressed sensing implementation
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