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Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks

  • Rüdiger EhlersEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10482)

Abstract

We present an approach for the verification of feed-forward neural networks in which all nodes have a piece-wise linear activation function. Such networks are often used in deep learning and have been shown to be hard to verify for modern satisfiability modulo theory (SMT) and integer linear programming (ILP) solvers.

The starting point of our approach is the addition of a global linear approximation of the overall network behavior to the verification problem that helps with SMT-like reasoning over the network behavior. We present a specialized verification algorithm that employs this approximation in a search process in which it infers additional node phases for the non-linear nodes in the network from partial node phase assignments, similar to unit propagation in classical SAT solving. We also show how to infer additional conflict clauses and safe node fixtures from the results of the analysis steps performed during the search. The resulting approach is evaluated on collision avoidance and handwritten digit recognition case studies.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of Bremen and DFKI GmbHBremenGermany

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