CNN architecture extraction on edge GPU
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F24%3A00135461" target="_blank" >RIV/00216224:14330/24:00135461 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-61486-6_10" target="_blank" >http://dx.doi.org/10.1007/978-3-031-61486-6_10</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-61486-6_10" target="_blank" >10.1007/978-3-031-61486-6_10</a>
Alternative languages
Result language
angličtina
Original language name
CNN architecture extraction on edge GPU
Original language description
Neural networks have become popular due to their versatility and state-of-the-art results in many applications, such as image classification, natural language processing, speech recognition, forecasting, etc. These applications are also used in resource-constrained environments such as embedded devices. In this work, the susceptibility of neural network implementations to reverse engineering is explored on the NVIDIA Jetson Nano microcomputer via side-channel analysis. To this end, an architecture extraction attack is presented. In the attack, 15 popular convolutional neural network architectures (EfficientNets, MobileNets, NasNet, etc.) are implemented on the GPU of Jetson Nano and the electromagnetic radiation of the GPU is analyzed during the inference operation of the neural networks. The results of the analysis show that neural network architectures are easily distinguishable using deep learning-based side-channel analysis.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Intelligence in Hardware Security (AIHWS) Workshop (Satellite Workshops held in parallel with the 22nd International Conference on Applied Cryptography and Network Security, ACNS 2024)
ISBN
9783031614859
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
18
Pages from-to
158-175
Publisher name
Springer
Place of publication
Abu Dhabi
Event location
Abu Dhabi
Event date
Jan 1, 2024
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
001285569600010