The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A estimativa de profundidade monocular melhorou drasticamente devido ao desenvolvimento de redes neurais profundas (DNNs). No entanto, estudos recentes revelaram que DNNs para estimativa de profundidade monocular contêm vulnerabilidades que podem levar a estimativas incorretas quando perturbações são adicionadas à entrada. Este estudo investiga se DNNs para estimativa de profundidade monocular são vulneráveis a estimativas incorretas quando luz padronizada é projetada em um objeto usando um projetor de vídeo. Para tanto, este estudo propõe um método evolutivo de ataque adversário com esquema de avaliação multifidelidade que permite criar exemplos adversários sob condição de caixa preta enquanto suprime o custo computacional. Experimentos em cenas simuladas e reais mostraram que o padrão de luz projetado fez com que um DNN estimasse erroneamente os objetos, como se eles tivessem se movido para trás.
Renya DAIMO
Kagoshima University
Satoshi ONO
Kagoshima University
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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},}
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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 -