Pith Number

pith:DSEJQBMA

pith:2026:DSEJQBMAZCHPSDQKFKPVEQHUYG

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

Dusit Niyato, Jiacheng Wang, Jin Xu, Ning Wang, Pengyu Chen, Tao Xiang, Weiyang Li

Model forensics verifies AI model authenticity and detects tampering in wireless networks.

arxiv:2605.14387 v1 · 2026-05-14 · cs.CR · eess.SP

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The results show that model forensics can provide important support for anomaly assessment, provenance tracing and trustworthy operation in AI-native wireless networks.

C2weakest assumption

That the watermark authentication and backdoor detection workflows demonstrated in the RF fingerprinting case study can be implemented in real wireless environments without major performance loss or easy circumvention by adversaries.

C3one line summary

Model forensics offers a taxonomy of techniques for verifying AI model authenticity and detecting malicious functions in wireless networks, with concrete workflows shown in an RF fingerprinting case study to support anomaly assessment and provenance tracing.

References

15 extracted · 15 resolved · 0 Pith anchors

[1] Toward native ai in 6g standardization: The roadmap of semantic communication, 2026

[2] Overview of ai and communication for 6g network: fundamentals, challenges, and future research opportunities, 2025

[3] Towards secure intelligent o-ran architecture: vulnerabilities, threats and promising technical solutions using llms, 2025

[4] A survey on xai for 5g and beyond security: Technical aspects, challenges and research directions, 2024

[5] Founding the domain of ai forensics., 2020

Receipt and verification

First computed	2026-05-17T23:39:07.655187Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1c88980580c88ef90e0a2a9f5240f4c1aea258cfa71c5512c3679be8444616cb

Aliases

arxiv: 2605.14387 · arxiv_version: 2605.14387v1 · doi: 10.48550/arxiv.2605.14387 · pith_short_12: DSEJQBMAZCHP · pith_short_16: DSEJQBMAZCHPSDQK · pith_short_8: DSEJQBMA

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/DSEJQBMAZCHPSDQKFKPVEQHUYG \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1c88980580c88ef90e0a2a9f5240f4c1aea258cfa71c5512c3679be8444616cb

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "d4a51a4b50fd6812a32397bf4efc70a221dc5288a7659ba3014501e820592b0f",
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      "eess.SP"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CR",
    "submitted_at": "2026-05-14T05:11:09Z",
    "title_canon_sha256": "1f78b22e06c0cd4ad7f30427a2c4371e156dd55c5baac6b2d7c745b5c831fca1"
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  "source": {
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    "kind": "arxiv",
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