CNN architecture extraction on edge GPU
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 42 references that are in the map. First posted on arXiv 24 Jan 2024.
Builds on
20 of 42 references.
References · 20
- 2017Attention Is All You Needcited by 60,361
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2023GPT-4 Technical Reportcited by 18,396
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2016Densely Connected Convolutional Networkscited by 6,720
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
- 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
- 2017Squeeze-and-Excitation Networkscited by 4,789
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
- 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
- 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
- 2019Searching for MobileNetV3cited by 1,994
8 more
- 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,200
- 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,101
- 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
- 2013Network In Networkcited by 605
- 2018Deep Learning for Generic Object Detection: A Surveycited by 392
- 2019Open DNN Box by Power Side-Channel Attackcited by 28
- 2021Can one hear the shape of a neural network?: Snooping the GPU via Magnetic Side Channelcited by 8
- 2020Screen Gleaning: A Screen Reading TEMPEST Attack on Mobile Devices Exploiting an Electromagnetic Side Channelcited by 3
Built on by
Citing papers per year: 2024: 1.
1 paper.