Pith Number

pith:G5J4KVHS

pith:2026:G5J4KVHS3XSYPU5ZBULC5GTNE5

not attested not anchored not stored refs resolved

Enabling Adversarial Robustness in AI Models through Kubeflow MLOps

Antonios Makris, Ioannis Korontanis, Konstantinos Tserpes, Stavros Bouras

Kubeflow MLOps integration allows Kubernetes to detect adversarial attacks on AI models and automatically deploy defenses that recover accuracy.

arxiv:2605.15249 v1 · 2026-05-14 · cs.CR · cs.LG

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{G5J4KVHS3XSYPU5ZBULC5GTNE5}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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 experimental results indicate that the deployed defense robustifies the model, significantly recovering accuracy relative to the degradation caused by the attack.

C2weakest assumption

That a detected degradation in accuracy during inference reliably indicates an adversarial attack (rather than other causes) and that the PGD defense can be automatically deployed and applied effectively in the live environment.

C3one line summary

A Kubeflow-based MLOps architecture detects FGSM adversarial attacks on deployed AI models and automatically applies PGD-based adversarial training to recover accuracy.

References

27 extracted · 27 resolved · 5 Pith anchors

[1] Security aspects of container orchestration in kubernetes environments, 2025

[2] Navigating the landscape of kubernetes security threats and challenges, 2024

[3] Coevolution: A comprehensive trustworthy framework for connected machine learning and secure interconnected ai solutions, 2025

[4] B. Gajbhiye and P . K. G. Pandian. (2024) Managing vulnerabilities in containerized and kubernetes environments. SSRN. [Online]. Available: https://ssrn.com/abstract=4982847 2024

[5] Continuous trust and resilience in kubernetes: Ai-driven certiﬁcate governance combined with cis-aligned node security

Receipt and verification

First computed	2026-05-20T00:00:48.471460Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

3753c554f2dde587d3b90d162e9a6d2754c7aa2de63cc8d141e8b9a06f87cbb5

Aliases

arxiv: 2605.15249 · arxiv_version: 2605.15249v1 · doi: 10.48550/arxiv.2605.15249 · pith_short_12: G5J4KVHS3XSY · pith_short_16: G5J4KVHS3XSYPU5Z · pith_short_8: G5J4KVHS

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/G5J4KVHS3XSYPU5ZBULC5GTNE5 \
  | 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: 3753c554f2dde587d3b90d162e9a6d2754c7aa2de63cc8d141e8b9a06f87cbb5

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2e500de45a446de2d7fa7b9a4d76512c43f08bcbb250eaabc4368f3abb11faf8",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CR",
    "submitted_at": "2026-05-14T12:45:36Z",
    "title_canon_sha256": "52920dd098731078e0224f70f133c2e76bbf9df1e05487229d1bee79a43efdbd"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15249",
    "kind": "arxiv",
    "version": 1
  }
}