Model Forensics in AI-Native Wireless Networks: Taxonomy, Applications, and Case Study

TL;DR

This paper explores the emerging field of model forensics in AI-native wireless networks, focusing on verification, malicious detection, and practical workflows demonstrated through RF fingerprinting case studies.

Contribution

It provides a comprehensive taxonomy, discusses key applications, and presents concrete workflows for model forensics in wireless networks, including a detailed RF fingerprinting case study.

Findings

Model forensics supports anomaly detection and provenance tracing.

Watermark authentication and backdoor detection workflows are effective.

RF fingerprinting can verify model authenticity and identify malicious behavior.

Abstract

As artificial intelligence (AI) is increasingly embedded in wireless networks, models are becoming core components that influence signal processing, resource scheduling and network control. However, model anomalies, tampering and malicious functions also introduce new security risks. In this article, we focus on model forensics in AI-native wireless networks. Specifically, we first discuss key problems including model authenticity verification, malicious function identification and accountability tracing, and summarize the main categories of model forensics. We then explain the role of model forensics in AI-native wireless networks and review representative application scenarios. In the case study, we use RF fingerprinting as an example and present two concrete workflows based on watermark authentication and backdoor detection, illustrating how provenance authentication and malicious behavior identification can be implemented in practice. The results show that model forensics can provide important support for anomaly assessment, provenance tracing and trustworthy operation in AI-native wireless networks. Finally, we outline several promising directions for future research in this emerging area.