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

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.

Submitted to IEEE-CCECE2019

References in corpus (4)

• NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles
• Towards Proving the Adversarial Robustness of Deep Neural Networks
• Output Range Analysis for Deep Neural Networks
• Verification of Binarized Neural Networks via Inter-Neuron Factoring