Security of Deep Learning Methodologies: Challenges and Opportunities

TL;DR

This paper explores the security challenges and research opportunities related to deep learning methodologies like transfer learning, emphasizing their unique vulnerabilities and attack vectors that are less studied compared to traditional models.

Contribution

It highlights the security issues specific to deep learning methodologies such as transfer learning, which have been underexplored in existing research.

Findings

Identification of unique vulnerabilities in transfer learning

Discussion of potential attack strategies on deep learning methodologies

Outlining research opportunities for enhancing security

Abstract

Despite the plethora of studies about security vulnerabilities and defenses of deep learning models, security aspects of deep learning methodologies, such as transfer learning, have been rarely studied. In this article, we highlight the security challenges and research opportunities of these methodologies, focusing on vulnerabilities and attacks unique to them.