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Security Analysis of Capsule Network Inference using Horizontal Collaboration by Adeyemo, Adewale; Khalid, Faiq; Odetola, Tolulope A.; Hasan, Syed Rafay is a scholarly article available to read on EtoBox.

What is Security Analysis of Capsule Network Inference using Horizontal Collaboration about?

The traditional convolution neural networks (CNN) have several drawbacks like the Picasso effect and the loss of information by the pooling layer. The Capsule network (CapsNet) was proposed to address these challenges because its architecture can encode and preserve the spatial orientation of input images. Similar to traditional CNNs, CapsNet is also vulnerable to several malicious attacks, as studied by several researchers in the literature. However, most of these studies focus on single-device-based inference, but horizontally collaborative inference in state-of-the-art systems, like intelligent edge services in self-driving cars, voice controllable systems, and drones, nullify most of these analyses. Horizontal collaboration implies partitioning the trained CNN models or CNN tasks to multiple end devices or edge nodes. Therefore, it is imperative to examine the robustness of the CapsNet against malicious attacks when deployed in horizontally collaborative environments. Towards this, we examine the robustness of the CapsNet when subjected to noise-based inference attacks in a horizontal collaborative environment. In this analysis, we perturbed the feature maps of the different la

Author

Adeyemo, Adewale; Khalid, Faiq; Odetola, Tolulope A.; Hasan, Syed Rafay

Published

2021

Language

EN