Model Forensics in AI-Native Wireless Networks: Taxonomy, Applications, and Case Study
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913127027703808 |
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| author | Chen, Pengyu Li, Weiyang Xu, Jin Wang, Jiacheng Wang, Ning Niyato, Dusit Xiang, Tao |
| author_facet | Chen, Pengyu Li, Weiyang Xu, Jin Wang, Jiacheng Wang, Ning Niyato, Dusit Xiang, Tao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14387 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Model Forensics in AI-Native Wireless Networks: Taxonomy, Applications, and Case Study Chen, Pengyu Li, Weiyang Xu, Jin Wang, Jiacheng Wang, Ning Niyato, Dusit Xiang, Tao Cryptography and Security Signal Processing 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. |
| title | Model Forensics in AI-Native Wireless Networks: Taxonomy, Applications, and Case Study |
| topic | Cryptography and Security Signal Processing |
| url | https://arxiv.org/abs/2605.14387 |