70 research outputs found
Distributionally Adversarial Attack
Recent work on adversarial attack has shown that Projected Gradient Descent
(PGD) Adversary is a universal first-order adversary, and the classifier
adversarially trained by PGD is robust against a wide range of first-order
attacks. It is worth noting that the original objective of an attack/defense
model relies on a data distribution , typically in the form of
risk maximization/minimization, e.g.,
with
some unknown data distribution and a loss
function. However, since PGD generates attack samples independently for each
data sample based on , the procedure does not necessarily
lead to good generalization in terms of risk optimization. In this paper, we
achieve the goal by proposing distributionally adversarial attack (DAA), a
framework to solve an optimal {\em adversarial-data distribution}, a perturbed
distribution that satisfies the constraint but deviates from the
original data distribution to increase the generalization risk maximally.
Algorithmically, DAA performs optimization on the space of potential data
distributions, which introduces direct dependency between all data points when
generating adversarial samples. DAA is evaluated by attacking state-of-the-art
defense models, including the adversarially-trained models provided by {\em MIT
MadryLab}. Notably, DAA ranks {\em the first place} on MadryLab's white-box
leaderboards, reducing the accuracy of their secret MNIST model to
(with perturbations of ) and the accuracy of their
secret CIFAR model to (with perturbations of ). Code for the experiments is released on
\url{https://github.com/tianzheng4/Distributionally-Adversarial-Attack}.Comment: accepted to AAAI-1
A Test Case Generation Method for Workflow Systems Based on I/O_WF_Net
At present, the testing of the workflow system is mainly based on manual testing, and the functions of only some tools are relatively simple. The design of test cases mainly depends on the experience of testers, which makes the lack of test coverage. In this paper, a test case generation method based on the I/O_WF_Net model is proposed. A test case generation algorithm that satisfies the process branch coverage criterion is designed, which solves the problem of automatic test case generation for workflow systems. The algorithm divides the model according to "split-merge pairs" to generate a decomposition tree of the model, and then traverses the tree to generate test cases. A workflow system modelling and test case generation tool are designed and implemented, and an actual workflow system is used as the experimental object to verify the effectiveness of the method
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