IEICE TRANSACTIONS on Information
Projection-Based Physical Adversarial Attack for Monocular Depth Estimation
Summary :
Monocular depth estimation has improved drastically due to the development of deep neural networks (DNNs). However, recent studies have revealed that DNNs for monocular depth estimation contain vulnerabilities that can lead to misestimation when perturbations are added to input. This study investigates whether DNNs for monocular depth estimation is vulnerable to misestimation when patterned light is projected on an object using a video projector. To this end, this study proposes an evolutionary adversarial attack method with multi-fidelity evaluation scheme that allows creating adversarial examples under black-box condition while suppressing the computational cost. Experiments in both simulated and real scenes showed that the designed light pattern caused a DNN to misestimate objects as if they have moved to the back.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.1 pp.31-35
- Publication Date
- 2023/01/01
- Publicized
- 2022/10/17
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022MUL0001
- Type of Manuscript
- Special Section LETTER (Special Section on Enriched Multimedia--Advanced Safety, Security and Convenience--)
- Category
Authors
Renya DAIMO
Kagoshima University
Satoshi ONO
Kagoshima University
Keyword
Latest Issue
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Renya DAIMO, Satoshi ONO, "Projection-Based Physical Adversarial Attack for Monocular Depth Estimation" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 1, pp. 31-35, January 2023, doi: 10.1587/transinf.2022MUL0001.
Abstract: Monocular depth estimation has improved drastically due to the development of deep neural networks (DNNs). However, recent studies have revealed that DNNs for monocular depth estimation contain vulnerabilities that can lead to misestimation when perturbations are added to input. This study investigates whether DNNs for monocular depth estimation is vulnerable to misestimation when patterned light is projected on an object using a video projector. To this end, this study proposes an evolutionary adversarial attack method with multi-fidelity evaluation scheme that allows creating adversarial examples under black-box condition while suppressing the computational cost. Experiments in both simulated and real scenes showed that the designed light pattern caused a DNN to misestimate objects as if they have moved to the back.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022MUL0001/_p
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@ARTICLE{e106-d_1_31,
author={Renya DAIMO, Satoshi ONO, },
journal={IEICE TRANSACTIONS on Information},
title={Projection-Based Physical Adversarial Attack for Monocular Depth Estimation},
year={2023},
volume={E106-D},
number={1},
pages={31-35},
abstract={Monocular depth estimation has improved drastically due to the development of deep neural networks (DNNs). However, recent studies have revealed that DNNs for monocular depth estimation contain vulnerabilities that can lead to misestimation when perturbations are added to input. This study investigates whether DNNs for monocular depth estimation is vulnerable to misestimation when patterned light is projected on an object using a video projector. To this end, this study proposes an evolutionary adversarial attack method with multi-fidelity evaluation scheme that allows creating adversarial examples under black-box condition while suppressing the computational cost. Experiments in both simulated and real scenes showed that the designed light pattern caused a DNN to misestimate objects as if they have moved to the back.},
keywords={},
doi={10.1587/transinf.2022MUL0001},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Projection-Based Physical Adversarial Attack for Monocular Depth Estimation
T2 - IEICE TRANSACTIONS on Information
SP - 31
EP - 35
AU - Renya DAIMO
AU - Satoshi ONO
PY - 2023
DO - 10.1587/transinf.2022MUL0001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2023
AB - Monocular depth estimation has improved drastically due to the development of deep neural networks (DNNs). However, recent studies have revealed that DNNs for monocular depth estimation contain vulnerabilities that can lead to misestimation when perturbations are added to input. This study investigates whether DNNs for monocular depth estimation is vulnerable to misestimation when patterned light is projected on an object using a video projector. To this end, this study proposes an evolutionary adversarial attack method with multi-fidelity evaluation scheme that allows creating adversarial examples under black-box condition while suppressing the computational cost. Experiments in both simulated and real scenes showed that the designed light pattern caused a DNN to misestimate objects as if they have moved to the back.
ER -
English
