pith:NTVX2QFG
Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection
Decomposing features via SVD into orthogonal parts lets detectors freeze general pre-trained knowledge and adapt only the rest to spot AI fakes without overfitting.
arxiv:2411.15633 v4 · 2024-11-23 · cs.CV
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{NTVX2QFGGXF3YBDNID45X54RGW}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
Claims
By employing Singular Value Decomposition (SVD) to decompose the original feature space into two orthogonal subspaces and freezing the principal components while adapting only the remained components, we preserve the pre-trained knowledge while learning fake patterns, effectively minimizing overfitting and enhancing generalization.
The principal components obtained from SVD on features of pre-trained vision foundation models capture general knowledge that remains useful and orthogonal to the specific patterns needed for detecting fakes, such that freezing them does not remove information critical for the detection task.
Orthogonal subspace decomposition via SVD on vision foundation model features preserves high-rank pre-trained knowledge by freezing principal components and adapting residuals, reducing overfitting for better generalization in AI-generated image detection.
References
Formal links
Cited by
Receipt and verification
| First computed | 2026-05-17T23:37:42.503015Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
6ceb7d40a635cbbc046d40f9dbf791359730559d773de73f6faa62963527dde1
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/NTVX2QFGGXF3YBDNID45X54RGW \
| 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: 6ceb7d40a635cbbc046d40f9dbf791359730559d773de73f6faa62963527dde1
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "439fd46a386a914a6a76f029ae6262bf358b60d175f9cd814cb6f494a0d828ed",
"cross_cats_sorted": [],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.CV",
"submitted_at": "2024-11-23T19:10:32Z",
"title_canon_sha256": "8562b9d00d4e88e6d8d323576b11263ac5229bdb152520a874df7728dfa341ae"
},
"schema_version": "1.0",
"source": {
"id": "2411.15633",
"kind": "arxiv",
"version": 4
}
}