Formal methods and software engineering for DL. Security, safety and productivity for DL systems development

Gaetan J. D. R. Hains; Arvid Jakobsson; Youry Khmelevsky

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arXiv · 1901.11334

Formal methods and software engineering for DL. Security, safety and productivity for DL systems development

Gaetan J. D. R. Hains·Arvid Jakobsson·Youry Khmelevsky

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Abstract

Deep Learning (DL) techniques are now widespread and being integrated into many important systems. Their classification and recognition abilities ensure their relevance for multiple application domains. As machine-learning that relies on training instead of algorithm programming, they offer a high degree of productivity. But they can be vulnerable to attacks and the verification of their correctness is only just emerging as a scientific and engineering possibility. This paper is a major update of a previously-published survey, attempting to cover all recent publications in this area. It also covers an even more recent trend, namely the design of domain-specific languages for producing and training neural nets.

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Gaetan J. D. R. Hains, Arvid Jakobsson, Youry Khmelevsky. 2019-01-31. Formal methods and software engineering for DL. Security, safety and productivity for DL systems development. https://arxiv.org/abs/1901.11334

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