pith:PQNPR6YU
Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction
High-level semantics act as a structural prior to reconstruct a unified yet discriminative artifact feature space, enabling a practical monolithic model for fake image detection.
arxiv:2602.06676 v4 · 2026-02-06 · cs.CV
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Record completeness
Claims
SICA outperforms 15 state-of-the-art methods and reconstructs the target unified-yet-discriminative artifact feature space in a near-orthogonal manner, thus firmly validating our hypothesis.
High-level semantics can serve as a structural prior for the reconstruction of the artifact feature space.
SICA uses semantic-induced constrained adaptation to build the first monolithic fake image detector that reconstructs a unified-yet-discriminative artifact feature space and outperforms 15 prior methods on a new dataset.
Formal links
Receipt and verification
| First computed | 2026-05-22T01:03:56.725837Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
7c1af8fb147cea9f0a946951f066f675689b854301589ed5423faca10a2c70b9
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/PQNPR6YUPTVJ6CUUNFI7AZXWOV \
| 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: 7c1af8fb147cea9f0a946951f066f675689b854301589ed5423faca10a2c70b9
Canonical record JSON
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