Improving Deep Learning Model Robustness Against Adversarial Attack by Increasing the Network Capacity

Marco Marchetti, Edmond S. L. Ho*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Nowadays, we are more and more reliant on Deep Learning (DL) models and thus it is essential to safeguard the security of these systems. This paper explores the security issues in Deep Learning and analyses, through the use of experiments, the way forward to build more resilient models. Experiments are conducted to identify the strengths and weaknesses of a new approach to improve the robustness of DL models against adversarial attacks. The results show improvements and new ideas that can be used as recommendations for researchers and practitioners to create increasingly better DL algorithms.
Original languageEnglish
Title of host publicationThe International conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies CCSET2022
Publication statusAccepted/In press - 11 Apr 2022
EventInternational conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies - Prince Sultan University, Saudi Arabia
Duration: 10 May 202211 May 2022

Conference

ConferenceInternational conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies
Abbreviated titleCCSET2022
Country/TerritorySaudi Arabia
Period10/05/2211/05/22

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