loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Mohamed Ibn Khedher 1 ; Hatem Ibn-Khedher 2 and Makhlouf Hadji 2

Affiliations: 1 IRT - SystemX, 8 Avenue de la Vauve, 91120 Palaiseau, France ; 2 Université de Paris, Lipade, F-75006 Paris, France

Keyword(s): Feed-forward Neural Network, Neural Network Verification, Big-M Optimization, Robustness.

Abstract: Deep neural networks have widely used for dealing with complex real-world problems. However, a major concern in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. Verifying its behavior means study the evolution of its outputs depending on the variation of its inputs. This verification is crucial in an uncertain environment where neural network inputs are noisy. In this paper, we propose an efficient technique for verifying feed-forward neural networks properties. In order to quantify the behavior of the proposed algorithm, we introduce different neural network scenarios to highlight the robustness according to predefined metrics and constraints. The proposed technique is based on the linearization of the non-convex Rectified Linear Unit (ReLU) activation function using the Big-M optimization approach. Moreover, we contribute by an iterative process to find the largest input range verifying (and then defining) the neu ral network proprieties of neural networks. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.135.184.250

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Khedher, M.; Ibn-Khedher, H. and Hadji, M. (2021). Dynamic and Scalable Deep Neural Network Verification Algorithm. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 1122-1130. DOI: 10.5220/0010323811221130

@conference{icaart21,
author={Mohamed Ibn Khedher. and Hatem Ibn{-}Khedher. and Makhlouf Hadji.},
title={Dynamic and Scalable Deep Neural Network Verification Algorithm},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2021},
pages={1122-1130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010323811221130},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Dynamic and Scalable Deep Neural Network Verification Algorithm
SN - 978-989-758-484-8
IS - 2184-433X
AU - Khedher, M.
AU - Ibn-Khedher, H.
AU - Hadji, M.
PY - 2021
SP - 1122
EP - 1130
DO - 10.5220/0010323811221130
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic