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The Octatope Abstract Domain for Verification of Neural Networks

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Formal Methods (FM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14000))

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Abstract

Efficient verification algorithms for neural networks often depend on various abstract domains such as intervals, zonotopes, and linear star sets. The choice of the abstract domain presents an expressiveness vs. scalability trade-off: simpler domains are less precise but yield faster algorithms. This paper investigates the octatope abstract domain in the context of neural net verification. Octatopes are affine transformations of n-dimensional octagons—sets of unit-two-variable-per-inequality (utvpi) constraints. Octatopes generalize the idea of zonotopes which can be viewed as an affine transformation of a box. On the other hand, octatopes can be considered as a restriction of linear star set, which are affine transformations of arbitrary \(\mathcal {H}\)-Polytopes. This distinction places octatopes firmly between zonotopes and star sets in their expressive power, but what about the efficiency of decision procedures?

An important analysis problem for neural networks is the exact range computation problem that asks to compute the exact set of possible outputs given a set of possible inputs. For this, three computational procedures are needed: 1) optimization of a linear cost function; 2) affine mapping; and 3) over-approximating the intersection with a half-space. While zonotopes allow an efficient solution for these approaches, star sets solves these procedures via linear programming. We show that these operations are faster for octatopes than the more expressive linear star sets. For octatopes, we reduce these problems to min-cost flow problems, which can be solved in strongly polynomial time using the Out-of-Kilter algorithm. Evaluating exact range computation on several ACAS Xu neural network benchmarks, we find that octatopes show promise as a practical abstract domain for neural network verification.

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Acknowledgment

This material is based upon work supported by the Air Force Office of Scientific Research and the Office of Naval Research under award numbers FA9550-19-1-0288, FA9550-21-1-0121, FA9550-22-1-0450, FA9550-22-1-0029 and N00014-22-1-2156. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force or the United States Navy. This research was supported in part by the Air Force Research Laboratory Information Directorate, through the Air Force Office of Scientific Research Summer Faculty Fellowship Program, Contract Numbers FA8750-15-3-6003, FA9550-15-0001 and FA9550-20-F-0005.

This work is also supported by the National Science Foundation (NSF) grant CCF-2009022 and by NSF CAREER award CCF-2146563.

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Authors and Affiliations

  1. Stony Brook University, Stony Brook, NY, USA

    Stanley Bak

  2. University of Colorado, Boulder, CO, USA

    Taylor Dohmen, Ashutosh Trivedi & Alvaro Velasquez

  3. West Virginia University, Morgantown, WV, USA

    K. Subramani & Piotr Wojciechowski

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  1. Stanley Bak
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  2. Taylor Dohmen
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  4. Ashutosh Trivedi
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  5. Alvaro Velasquez
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  6. Piotr Wojciechowski
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Correspondence to Ashutosh Trivedi .

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  1. University of Toronto, Toronto, ON, Canada

    Marsha Chechik

  2. RWTH Aachen University, Aachen, Germany

    Joost-Pieter Katoen

  3. University of Lübeck, Lübeck, Germany

    Martin Leucker

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Bak, S., Dohmen, T., Subramani, K., Trivedi, A., Velasquez, A., Wojciechowski, P. (2023). The Octatope Abstract Domain for Verification of Neural Networks. In: Chechik, M., Katoen, JP., Leucker, M. (eds) Formal Methods. FM 2023. Lecture Notes in Computer Science, vol 14000. Springer, Cham. https://doi.org/10.1007/978-3-031-27481-7_26

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