390 research outputs found
Constraint-based reachability
Iterative imperative programs can be considered as infinite-state systems
computing over possibly unbounded domains. Studying reachability in these
systems is challenging as it requires to deal with an infinite number of states
with standard backward or forward exploration strategies. An approach that we
call Constraint-based reachability, is proposed to address reachability
problems by exploring program states using a constraint model of the whole
program. The keypoint of the approach is to interpret imperative constructions
such as conditionals, loops, array and memory manipulations with the
fundamental notion of constraint over a computational domain. By combining
constraint filtering and abstraction techniques, Constraint-based reachability
is able to solve reachability problems which are usually outside the scope of
backward or forward exploration strategies. This paper proposes an
interpretation of classical filtering consistencies used in Constraint
Programming as abstract domain computations, and shows how this approach can be
used to produce a constraint solver that efficiently generates solutions for
reachability problems that are unsolvable by other approaches.Comment: In Proceedings Infinity 2012, arXiv:1302.310
Robustness Verification of Support Vector Machines
We study the problem of formally verifying the robustness to adversarial
examples of support vector machines (SVMs), a major machine learning model for
classification and regression tasks. Following a recent stream of works on
formal robustness verification of (deep) neural networks, our approach relies
on a sound abstract version of a given SVM classifier to be used for checking
its robustness. This methodology is parametric on a given numerical abstraction
of real values and, analogously to the case of neural networks, needs neither
abstract least upper bounds nor widening operators on this abstraction. The
standard interval domain provides a simple instantiation of our abstraction
technique, which is enhanced with the domain of reduced affine forms, which is
an efficient abstraction of the zonotope abstract domain. This robustness
verification technique has been fully implemented and experimentally evaluated
on SVMs based on linear and nonlinear (polynomial and radial basis function)
kernels, which have been trained on the popular MNIST dataset of images and on
the recent and more challenging Fashion-MNIST dataset. The experimental results
of our prototype SVM robustness verifier appear to be encouraging: this
automated verification is fast, scalable and shows significantly high
percentages of provable robustness on the test set of MNIST, in particular
compared to the analogous provable robustness of neural networks
Automatic modular abstractions for template numerical constraints
We propose a method for automatically generating abstract transformers for
static analysis by abstract interpretation. The method focuses on linear
constraints on programs operating on rational, real or floating-point variables
and containing linear assignments and tests. In addition to loop-free code, the
same method also applies for obtaining least fixed points as functions of the
precondition, which permits the analysis of loops and recursive functions. Our
algorithms are based on new quantifier elimination and symbolic manipulation
techniques. Given the specification of an abstract domain, and a program block,
our method automatically outputs an implementation of the corresponding
abstract transformer. It is thus a form of program transformation. The
motivation of our work is data-flow synchronous programming languages, used for
building control-command embedded systems, but it also applies to imperative
and functional programming
On Sound Relative Error Bounds for Floating-Point Arithmetic
State-of-the-art static analysis tools for verifying finite-precision code
compute worst-case absolute error bounds on numerical errors. These are,
however, often not a good estimate of accuracy as they do not take into account
the magnitude of the computed values. Relative errors, which compute errors
relative to the value's magnitude, are thus preferable. While today's tools do
report relative error bounds, these are merely computed via absolute errors and
thus not necessarily tight or more informative. Furthermore, whenever the
computed value is close to zero on part of the domain, the tools do not report
any relative error estimate at all. Surprisingly, the quality of relative error
bounds computed by today's tools has not been systematically studied or
reported to date. In this paper, we investigate how state-of-the-art static
techniques for computing sound absolute error bounds can be used, extended and
combined for the computation of relative errors. Our experiments on a standard
benchmark set show that computing relative errors directly, as opposed to via
absolute errors, is often beneficial and can provide error estimates up to six
orders of magnitude tighter, i.e. more accurate. We also show that interval
subdivision, another commonly used technique to reduce over-approximations, has
less benefit when computing relative errors directly, but it can help to
alleviate the effects of the inherent issue of relative error estimates close
to zero
Efficient Synthesis of Room Acoustics via Scattering Delay Networks
An acoustic reverberator consisting of a network of delay lines connected via
scattering junctions is proposed. All parameters of the reverberator are
derived from physical properties of the enclosure it simulates. It allows for
simulation of unequal and frequency-dependent wall absorption, as well as
directional sources and microphones. The reverberator renders the first-order
reflections exactly, while making progressively coarser approximations of
higher-order reflections. The rate of energy decay is close to that obtained
with the image method (IM) and consistent with the predictions of Sabine and
Eyring equations. The time evolution of the normalized echo density, which was
previously shown to be correlated with the perceived texture of reverberation,
is also close to that of IM. However, its computational complexity is one to
two orders of magnitude lower, comparable to the computational complexity of a
feedback delay network (FDN), and its memory requirements are negligible
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