loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Author: Kevin Swingler

Affiliation: Computing Sciene and Mathematics, University of Stirling, Stirling, FK9 4LA, U.K.

Keyword(s): Adversarial Images, Model Robustness, Degraded Image Performance.

Abstract: Convolutional Neural Networks (CNN) are extremely popular for modelling sound and images, but they suffer from a lack of robustness that could threaten their usefulness in applications where reliability is important. Recent studies have shown how it is possible to maliciously create adversarial images—those that appear to the human observer as perfect examples of one class but that fool a CNN into assigning them to a different, incorrect class. It takes some effort to make these images as they need to be designed specifically to fool a given network. In this paper we show that images can be degraded in a number of simple ways that do not need careful design and that would not affect the ability of a human observer, but which cause severe deterioration in the performance of three different CNN models. We call the speed of the deterioration in performance due to incremental degradations in image quality the degradation profile of a model and argue that reporting the degradation profile is as important as reporting performance on clean images. (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 18.217.6.114

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:
Swingler, K. (2022). A Suite of Incremental Image Degradation Operators for Testing Image Classification Algorithms. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA; ISBN 978-989-758-611-8; ISSN 2184-3236, SciTePress, pages 262-272. DOI: 10.5220/0011511000003332

@conference{ncta22,
author={Kevin Swingler.},
title={A Suite of Incremental Image Degradation Operators for Testing Image Classification Algorithms},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA},
year={2022},
pages={262-272},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011511000003332},
isbn={978-989-758-611-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA
TI - A Suite of Incremental Image Degradation Operators for Testing Image Classification Algorithms
SN - 978-989-758-611-8
IS - 2184-3236
AU - Swingler, K.
PY - 2022
SP - 262
EP - 272
DO - 10.5220/0011511000003332
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