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

TL;DR

This paper reviews security vulnerabilities in deep learning models, discusses future risks, and proposes mitigation strategies with evaluation metrics to enhance the safe deployment of DL systems.

Contribution

It provides a comprehensive analysis of current and future security challenges in deep learning and introduces risk mitigation techniques with measurable effectiveness.

Findings

Identified key security vulnerabilities in current DL models.

Proposed mitigation strategies with evaluation metrics.

Analyzed future risks based on technological advancements.

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 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.