Deep Learning Under Siege: Identifying Security Vulnerabilities and Risk Mitigation Strategies

Proceedings of the 20th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP) 2024, Matsue Japan 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 cent...

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Bibliographic Details
Main Authors Al-Karaki, Jamal, Khan, Muhammad Al-Zafar, Mohamad, Mostafa, Chowdhury, Dababrata
Format Journal Article
LanguageEnglish
Published 14.09.2024
Subjects
Computer Science - Artificial Intelligence
Computer Science - Cryptography and Security
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Summary:Proceedings of the 20th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP) 2024, Matsue Japan 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 the 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:10.48550/arxiv.2409.09517