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

2026-05-14

AI Summary

This work addresses emerging security threats in AI-native wireless networks, where anomalous model behavior, tampering, and malicious functionalities necessitate robust mechanisms for authenticity verification, malicious activity detection, and accountability tracing. The paper presents the first systematic model forensic framework tailored to this domain, integrating watermark-based authentication, backdoor detection, and radio-frequency fingerprint analysis. It articulates the technical pathways and their operational logic within signal processing and network control contexts. Empirical validation through representative workflows demonstrates the framework’s effectiveness in tracing model provenance, assessing anomalies, and ensuring trustworthy model execution, thereby providing critical support for securing AI-native wireless systems.

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.

Problem

• model forensics
• AI-native wireless networks
• model authenticity
• malicious function identification
• accountability tracing

Innovation

• model forensics
• AI-native wireless networks
• watermark authentication
• backdoor detection
• RF fingerprinting