Multispectral Invisible Coating: Laminated Visible-Thermal Physical Attack Against Multispectral Object Detectors Using Transparent Low-E Films
Abstract
Multispectral object detection plays a vital role in safety-critical vision systems that require an around-the-clock operation and encounter dynamic real-world situations(e.g., self-driving cars and autonomous surveillance systems). Despite its crucial competence in safety-related applications, its security against physical attacks is severely understudied. We investigate the vulnerability of multispectral detectors against physical attacks by proposing a new physical method: Multispectral Invisible Coating. Utilizing transparent Low-e films, we realize a laminated visible-thermal physical attack by attaching Low-e films over a visible attack printing. Moreover, we apply our physical method to manufacture a Multispectral Invisible Suit that hides persons from the multiple view angles of Multispectral detectors. To simulate our attack under various surveillance scenes, we constructed a large-scale multispectral pedestrian dataset which we will release in public. Extensive experiments show that our proposed method effectively attacks the state-of-the-art multispectral detector both in the digital space and the physical world.
Cite
Text
Kim et al. "Multispectral Invisible Coating: Laminated Visible-Thermal Physical Attack Against Multispectral Object Detectors Using Transparent Low-E Films." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I1.25197Markdown
[Kim et al. "Multispectral Invisible Coating: Laminated Visible-Thermal Physical Attack Against Multispectral Object Detectors Using Transparent Low-E Films." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/kim2023aaai-multispectral/) doi:10.1609/AAAI.V37I1.25197BibTeX
@inproceedings{kim2023aaai-multispectral,
title = {{Multispectral Invisible Coating: Laminated Visible-Thermal Physical Attack Against Multispectral Object Detectors Using Transparent Low-E Films}},
author = {Kim, Taeheon and Yu, Youngjoon and Ro, Yong Man},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {1151-1159},
doi = {10.1609/AAAI.V37I1.25197},
url = {https://mlanthology.org/aaai/2023/kim2023aaai-multispectral/}
}