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Minimal Multi-Layer Modifications of Deep Neural Networks

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Software Verification and Formal Methods for ML-Enabled Autonomous Systems (NSV 2022, FoMLAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13466))

Abstract

Deep neural networks (DNNs) have become increasingly popular in recent years. However, despite their many successes, DNNs may also err and produce incorrect and potentially fatal outputs in safety-critical settings, such as autonomous driving, medical diagnosis, and airborne collision avoidance systems. Much work has been put into detecting such erroneous behavior in DNNs, e.g., via testing or verification, but removing these errors after their detection has received lesser attention. We present here a new tool, called 3M-DNN, for repairing a given DNN, which is known to err on some set of inputs. The novel repair procedure implemented in 3M-DNN computes a modification to the network’s weights that corrects its behavior, and attempts to minimize this change via a sequence of calls to a backend, black-box DNN verification engine. To the best of our knowledge, our method is the first one that allows repairing the network by simultaneously modifying multiple layers. This is achieved by splitting the network into sub-networks, and applying a single-layer repairing technique to each component. We evaluated 3M-DNN tool on an extensive set of benchmarks, obtaining promising results.

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Notes

  1. 1.

    https://zenodo.org/record/5735194#.Ysvf_nZByUk.

  2. 2.

    It may be possible that an invocation of SingleLayerModification() fails because no change is possible that obtains the desired results. Whenever this happens, 3M-DNN continues to the next iteration, exploring a different change to the separation layers’ values. This situation is theoretically possible, but did not occur in our experiments.

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Acknowledgement

This work was partially supported by the Israel Science Foundation (grant number 683/18) and the HUJI Federmann Cyber Security Center.

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Authors and Affiliations

  1. The Hebrew University of Jerusalem, Jerusalem, Israel

    Idan Refaeli & Guy Katz

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  1. Idan Refaeli
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  2. Guy Katz
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Correspondence to Guy Katz .

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Editors and Affiliations

  1. The Hebrew University of Jerusalem, Jerusalem, Israel

    Omri Isac

  2. Rensselaer Polytechnic Institute, New York, NY, USA

    Radoslav Ivanov

  3. The Hebrew University of Jerusalem, Jerusalem, Israel

    Guy Katz

  4. Palo Alto, CA, USA

    Nina Narodytska

  5. University of Trieste, Trieste, Italy

    Laura Nenzi

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Refaeli, I., Katz, G. (2022). Minimal Multi-Layer Modifications of Deep Neural Networks. In: Isac, O., Ivanov, R., Katz, G., Narodytska, N., Nenzi, L. (eds) Software Verification and Formal Methods for ML-Enabled Autonomous Systems. NSV FoMLAS 2022 2022. Lecture Notes in Computer Science, vol 13466. Springer, Cham. https://doi.org/10.1007/978-3-031-21222-2_4

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