Security Assessment of Selectively Encrypted Visual Data: Iris Recognition on Protected Samples

Martin Rieger; Jutta Hämmerle-Uhl; Andreas Uhl

doi:10.1109/ICIP42928.2021.9506294

Security Assessment of Selectively Encrypted Visual Data: Iris Recognition on Protected Samples

Martin Rieger, Jutta Hämmerle-Uhl, Andreas Uhl

Artificial Intelligence and Human Interfaces

Research output: Chapter in Book/Report/Conference proceeding/Legal commentary › Conference contribution › peer-review

Abstract

Security assessment of partially encrypted visual data is known to be difficult. We show that iris recognition performance on encrypted sample data turns out to be a good predictor for protection strength of encryption schemes for visual data, in particular in settings where image structure and intelligibility is preserved to some extent. This is of particular interest, as common image quality metrics used for this task exhibit specifically poor prediction quality in this setting.

Original language	English
Title of host publication	2021 IEEE International Conference on Image Processing (ICIP)
Publisher	IEEE
Pages	3008-3012
Number of pages	5
ISBN (Print)	978-1-6654-3102-6
DOIs	https://doi.org/10.1109/ICIP42928.2021.9506294
Publication status	Published - 22 Sept 2021
Event	2021 IEEE International Conference on Image Processing (ICIP) - Anchorage, AK, USA Duration: 19 Sept 2021 → 22 Sept 2021

Conference

Conference	2021 IEEE International Conference on Image Processing (ICIP)
Period	19/09/21 → 22/09/21

Keywords

Legged locomotion
Image quality
Visualization
Correlation
Sensitivity
Image recognition
Conferences

Fields of Science and Technology Classification 2012

102 Computer Sciences

Access to Document

10.1109/ICIP42928.2021.9506294

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9506294

Cite this

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title = "Security Assessment of Selectively Encrypted Visual Data: Iris Recognition on Protected Samples",

abstract = "Security assessment of partially encrypted visual data is known to be difficult. We show that iris recognition performance on encrypted sample data turns out to be a good predictor for protection strength of encryption schemes for visual data, in particular in settings where image structure and intelligibility is preserved to some extent. This is of particular interest, as common image quality metrics used for this task exhibit specifically poor prediction quality in this setting.",

keywords = "Legged locomotion, Image quality, Visualization, Correlation, Sensitivity, Image recognition, Conferences",

author = "Martin Rieger and Jutta H{\"a}mmerle-Uhl and Andreas Uhl",

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day = "22",

doi = "10.1109/ICIP42928.2021.9506294",

language = "English",

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AU - Rieger, Martin

AU - Hämmerle-Uhl, Jutta

AU - Uhl, Andreas

PY - 2021/9/22

Y1 - 2021/9/22

N2 - Security assessment of partially encrypted visual data is known to be difficult. We show that iris recognition performance on encrypted sample data turns out to be a good predictor for protection strength of encryption schemes for visual data, in particular in settings where image structure and intelligibility is preserved to some extent. This is of particular interest, as common image quality metrics used for this task exhibit specifically poor prediction quality in this setting.

AB - Security assessment of partially encrypted visual data is known to be difficult. We show that iris recognition performance on encrypted sample data turns out to be a good predictor for protection strength of encryption schemes for visual data, in particular in settings where image structure and intelligibility is preserved to some extent. This is of particular interest, as common image quality metrics used for this task exhibit specifically poor prediction quality in this setting.

KW - Legged locomotion

KW - Image quality

KW - Visualization

KW - Correlation

KW - Sensitivity

KW - Image recognition

KW - Conferences

UR - https://ieeexplore.ieee.org/document/9506294/

UR - https://www.mendeley.com/catalogue/fb467186-3ade-3bea-8ab2-6cf65726c470/

U2 - 10.1109/ICIP42928.2021.9506294

DO - 10.1109/ICIP42928.2021.9506294

M3 - Conference contribution

SN - 978-1-6654-3102-6

SP - 3008

EP - 3012

BT - 2021 IEEE International Conference on Image Processing (ICIP)

PB - IEEE

T2 - 2021 IEEE International Conference on Image Processing (ICIP)

Y2 - 19 September 2021 through 22 September 2021

ER -

Security Assessment of Selectively Encrypted Visual Data: Iris Recognition on Protected Samples

Abstract

Conference

Keywords

Fields of Science and Technology Classification 2012

Access to Document

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Cite this