Pith Number

pith:G4ATTNW5

pith:2026:G4ATTNW5LSHMNNDQMWG4EOSQFK

not attested not anchored not stored refs pending

AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images

Bo Zhang, Haihong E, Haiyang Sun, Haocheng Gao, Jiacheng Liu, Junpeng Ding, Liangjia Wang, Peilin Gao, Ronghui Xi, Tzu-Yen Ma, Yiling Huang, Yizhuo Zhao, Yuan Liu, Yuanze Li, Yujie Wang, Yuyue Zhang, Zhongjun Yang, Zichen Tang, Zijie Xi, Zirui Wang, Zixin Ding

AEGIS benchmark reveals that even advanced models detect AI-generated academic images at only 48.80 percent overall accuracy.

arxiv:2604.28177 v2 · 2026-04-30 · cs.CV · cs.CY

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\usepackage{pith}
\pithnumber{G4ATTNW5LSHMNNDQMWG4EOSQFK}

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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

AEGIS serves as a diagnostic testbed exposing fundamental limitations in academic image forensics, with even GPT-5.1 reaching only 48.80% overall performance and expert models limited to 30.09% IoU localization accuracy.

C2weakest assumption

The 39 fine-grained academic subtypes and four simulated forgery strategies using 25 generative models sufficiently represent the real-world distribution and difficulty of AI-generated academic images.

C3one line summary

AEGIS benchmark reveals that leading AI models achieve only 48.80% overall accuracy and low localization precision when analyzing AI-generated academic images, exposing gaps between generative and forensic capabilities.

Receipt and verification

First computed	2026-05-22T01:04:03.724447Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

370139b6dd5c8ec6b470658dc23a502aa63ce6c219119e0e13ed174f5210fcbb

Aliases

arxiv: 2604.28177 · arxiv_version: 2604.28177v2 · doi: 10.48550/arxiv.2604.28177 · pith_short_12: G4ATTNW5LSHM · pith_short_16: G4ATTNW5LSHMNNDQ · pith_short_8: G4ATTNW5

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/G4ATTNW5LSHMNNDQMWG4EOSQFK \
  | 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: 370139b6dd5c8ec6b470658dc23a502aa63ce6c219119e0e13ed174f5210fcbb

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2c6db4aa71d650b7b7c3cb76ae018bed1fb5666150a8c1fd6479feb2c8502cfa",
    "cross_cats_sorted": [
      "cs.CY"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-04-30T17:56:58Z",
    "title_canon_sha256": "411c5c36ec103f0a9b843aac5d75953fc4368e686a18a5e5aa90cceee23ff54a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.28177",
    "kind": "arxiv",
    "version": 2
  }
}