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Authors: Rania Khalsi 1 ; Mallek Mziou Sallami 2 ; Imen Smati 1 and Faouzi Ghorbel 1

Affiliations: 1 CRISTAL Laboratory, GRIFT Research Group, Ecole Nationale des Sciences de l’Informatique (ENSI), La Manouba University, 2010, La Manouba, Tunisia ; 2 CEA, Evry, France

Keyword(s): Contours Classification, Abstract Interpretation, DNN Geometric Robustness, Uncertainty in AI.

Abstract: DNN certification using abstract interpretation often deals with image-type data, and subsequently evaluates the robustness of the deep classifiers against disturbances on the images such as geometric transformations, occlusion and convolutional noises by modeling them as an abstract domain. In this paper, we propose ContourVerifier, a new system for the evaluation of contour classifiers as we have formulated the abstract domains generated by rigid displacements on contours. This formulation allowed us to estimate the robustness of deep classifiers with different architectures and on different databases. This work will serve as a fundamental building block for the certification of deep models developed for shape recognition.

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Paper citation in several formats:
Khalsi, R.; Sallami, M.; Smati, I. and Ghorbel, F. (2022). ContourVerifier: A Novel System for the Robustness Evaluation of Deep Contour Classifiers. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 1003-1010. DOI: 10.5220/0010994500003116

@conference{icaart22,
author={Rania Khalsi. and Mallek Mziou Sallami. and Imen Smati. and Faouzi Ghorbel.},
title={ContourVerifier: A Novel System for the Robustness Evaluation of Deep Contour Classifiers},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={1003-1010},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010994500003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - ContourVerifier: A Novel System for the Robustness Evaluation of Deep Contour Classifiers
SN - 978-989-758-547-0
IS - 2184-433X
AU - Khalsi, R.
AU - Sallami, M.
AU - Smati, I.
AU - Ghorbel, F.
PY - 2022
SP - 1003
EP - 1010
DO - 10.5220/0010994500003116
PB - SciTePress

- Science and Technology Publications, Lda.
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