Cache Side-Channel Attacks Against Black-Box Image Processing Software

Ssuhung Yeh, Yuji Sekiya

2023

Abstract

Cache side-channel attacks are a persisting threat to modern computers for their ability to steal secret information in memory and hard-to-detect characteristics. While researchers have studied these attacks for a long time, there has been relatively little focus on attacks against media software. One reason is the inherent noisiness of cache side-channels, making it challenging to extract meaningful information from it. However, recent advancements in machine learning have changed the landscape, making side-channel analysis more accessible. In this paper, we proposed a new side-channel analysis framework that is capable of extracting high-level information from complex applications. With this framework, we attacked image processing programs, reconstructed images that the victim opened with cache side-channel attacks, and achieved significantly improved results compared to the previous work.

Paper Citation

in Harvard Style

Yeh S. and Sekiya Y. (2023). Cache Side-Channel Attacks Against Black-Box Image Processing Software. In Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS; ISBN 978-989-758-672-9, SciTePress, pages 578-584. DOI: 10.5220/0012264400003584

in Bibtex Style

@conference{dmmlacs23,
author={Ssuhung Yeh and Yuji Sekiya},
title={Cache Side-Channel Attacks Against Black-Box Image Processing Software},
booktitle={Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS},
year={2023},
pages={578-584},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012264400003584},
isbn={978-989-758-672-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS
TI - Cache Side-Channel Attacks Against Black-Box Image Processing Software
SN - 978-989-758-672-9
AU - Yeh S.
AU - Sekiya Y.
PY - 2023
SP - 578
EP - 584
DO - 10.5220/0012264400003584
PB - SciTePress