1,611 research outputs found
Maximum Resilience of Artificial Neural Networks
The deployment of Artificial Neural Networks (ANNs) in safety-critical
applications poses a number of new verification and certification challenges.
In particular, for ANN-enabled self-driving vehicles it is important to
establish properties about the resilience of ANNs to noisy or even maliciously
manipulated sensory input. We are addressing these challenges by defining
resilience properties of ANN-based classifiers as the maximal amount of input
or sensor perturbation which is still tolerated. This problem of computing
maximal perturbation bounds for ANNs is then reduced to solving mixed integer
optimization problems (MIP). A number of MIP encoding heuristics are developed
for drastically reducing MIP-solver runtimes, and using parallelization of
MIP-solvers results in an almost linear speed-up in the number (up to a certain
limit) of computing cores in our experiments. We demonstrate the effectiveness
and scalability of our approach by means of computing maximal resilience bounds
for a number of ANN benchmark sets ranging from typical image recognition
scenarios to the autonomous maneuvering of robots.Comment: Timestamp research work conducted in the project. version 2: fix some
typos, rephrase the definition, and add some more existing wor
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
In recent years, the notion of local robustness (or robustness for short) has
emerged as a desirable property of deep neural networks. Intuitively,
robustness means that small perturbations to an input do not cause the network
to perform misclassifications. In this paper, we present a novel algorithm for
verifying robustness properties of neural networks. Our method synergistically
combines gradient-based optimization methods for counterexample search with
abstraction-based proof search to obtain a sound and ({\delta}-)complete
decision procedure. Our method also employs a data-driven approach to learn a
verification policy that guides abstract interpretation during proof search. We
have implemented the proposed approach in a tool called Charon and
experimentally evaluated it on hundreds of benchmarks. Our experiments show
that the proposed approach significantly outperforms three state-of-the-art
tools, namely AI^2 , Reluplex, and Reluval
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