EdgeShield: A Universal and Efficient Edge Computing Framework for Robust AI

TL;DR

EdgeShield is a novel edge computing framework that efficiently detects adversarial attacks on AI systems in real-time, leveraging attention mechanisms and lightweight networks to ensure broad applicability and low computational cost.

Contribution

The paper introduces a universal, efficient edge framework with an attention-based detection method and lightweight network design for robust adversarial attack detection.

Findings

Achieves 97.43% F-score in attack detection

Reduces computational complexity and cost compared to previous methods

Effective across multiple neural network architectures

Abstract

The increasing prevalence of adversarial attacks on Artificial Intelligence (AI) systems has created a need for innovative security measures. However, the current methods of defending against these attacks often come with a high computing cost and require back-end processing, making real-time defense challenging. Fortunately, there have been remarkable advancements in edge-computing, which make it easier to deploy neural networks on edge devices. Building upon these advancements, we propose an edge framework design to enable universal and efficient detection of adversarial attacks. This framework incorporates an attention-based adversarial detection methodology and a lightweight detection network formation, making it suitable for a wide range of neural networks and can be deployed on edge devices. To assess the effectiveness of our proposed framework, we conducted evaluations on five neural networks. The results indicate an impressive 97.43% F-score can be achieved, demonstrating the framework's proficiency in detecting adversarial attacks. Moreover, our proposed framework also exhibits significantly reduced computing complexity and cost in comparison to previous detection methods. This aspect is particularly beneficial as it ensures that the defense mechanism can be efficiently implemented in real-time on-edge devices.