Deep Learning Under Siege: Identifying Security Vulnerabilities and Risk Mitigation Strategies
Document Type
Conference Proceeding
Source of Publication
Smart Innovation Systems and Technologies
Publication Date
1-1-2026
Abstract
With the rise in the wholesale adoption of Deep Learning (DL) models in nearly all aspects of society, a unique set of challenges is imposed. Primarily centered around the architectures of these models, these risks pose a significant challenge, and addressing these challenges is key to their successful implementation and usage in future. In this research, we present the security challenges associated with the current DL models deployed into production, as well as anticipate the challenges of future DL technologies based on the advancements in computing, AI, and hardware technologies. In addition, we propose risk mitigation techniques to inhibit these challenges and provide metrical evaluations to measure the effectiveness of these metrics.
DOI Link
ISBN
[9789819668588]
ISSN
Publisher
Springer Nature Singapore
Volume
119 SIST
First Page
185
Last Page
194
Disciplines
Computer Sciences
Keywords
Explainability, Interpretability, Model vulnerability, Security of Deep Learning
Scopus ID
Recommended Citation
Al-Karaki, Jamal; Al-Zafar Khan, Muhammad; Mohamad, Mostafa; and Chowdhury, Dababrata, "Deep Learning Under Siege: Identifying Security Vulnerabilities and Risk Mitigation Strategies" (2026). All Works. 7738.
https://zuscholars.zu.ac.ae/works/7738
Indexed in Scopus
yes
Open Access
no