Paper A v3: full rewrite for IEEE Access with three-method convergence

Major changes from v2:

Terminology:
- "digitally replicated" -> "non-hand-signed" throughout (per partner v3
  feedback and to avoid implicit accusation)
- "Firm A near-universal non-hand-signing" -> "replication-dominated"
  (per interview nuance: most but not all Firm A partners use replication)

Target journal: IEEE TAI -> IEEE Access (per NCKU CSIE list)

New methodological sections (III.G-III.L + IV.D-IV.G):
- Three convergent threshold methods (KDE antimode + Hartigan dip test /
  Burgstahler-Dichev McCrary / EM-fitted Beta mixture + logit-GMM
  robustness check)
- Explicit unit-of-analysis discussion (signature vs accountant)
- Accountant-level 2D Gaussian mixture (BIC-best K=3 found empirically)
- Pixel-identity validation anchor (no manual annotation needed)
- Low-similarity negative anchor + Firm A replication-dominated anchor

New empirical findings integrated:
- Firm A signature cosine UNIMODAL (dip p=0.17) - long left tail = minority
  hand-signers
- Full-sample cosine MULTIMODAL but not cleanly bimodal (BIC prefers 3-comp
  mixture) - signature-level is continuous quality spectrum
- Accountant-level mixture trimodal (C1 Deloitte-heavy 139/141,
  C2 other Big-4, C3 smaller firms). 2-comp crossings cos=0.945, dh=8.10
- Pixel-identity anchor (310 pairs) gives perfect recall at all cosine
  thresholds
- Firm A anchor rates: cos>0.95=92.5%, dual-rule cos>0.95 AND dh<=8=89.95%

New discussion section V.B: "Continuous-quality spectrum vs discrete-
behavior regimes" - the core interpretive contribution of v3.

References added: Hartigan & Hartigan 1985, Burgstahler & Dichev 1997,
McCrary 2008, Dempster-Laird-Rubin 1977, White 1982 (refs 37-41).

export_v3.py builds Paper_A_IEEE_Access_Draft_v3.docx (462 KB, +40% vs v2
from expanded methodology + results sections).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

paper/paper_a_abstract_v3.md

@@ -0,0 +1,17 @@
+# Abstract
+
+<!-- 200-270 words -->
+
+Regulations in many jurisdictions require Certified Public Accountants (CPAs) to attest to each audit report they certify, typically by affixing a signature or seal.
+However, the digitization of financial reporting makes it straightforward to reuse a stored signature image across multiple reports---whether by administrative stamping or firm-level electronic signing systems---potentially undermining the intent of individualized attestation.
+Unlike signature forgery, where an impostor imitates another person's handwriting, *non-hand-signed* reproduction involves the legitimate signer's own stored signature image being reproduced on each report, a practice that is visually invisible to report users and infeasible to audit at scale through manual inspection.
+We present an end-to-end AI pipeline that automatically detects non-hand-signed auditor signatures in financial audit reports.
+The pipeline integrates a Vision-Language Model for signature page identification, YOLOv11 for signature region detection, and ResNet-50 for deep feature extraction, followed by a dual-descriptor verification combining cosine similarity of deep embeddings with difference hashing (dHash).
+For threshold determination we apply three statistically independent methods---Kernel Density antimode with a Hartigan dip test, Burgstahler-Dichev/McCrary discontinuity, and EM-fitted Beta mixtures with a logit-Gaussian robustness check---at both the signature level and the accountant level.
+Applied to 90,282 audit reports filed in Taiwan over 2013--2023 (182,328 signatures from 758 CPAs) the three methods reveal an informative asymmetry: signature-level similarity forms a continuous quality spectrum with no clean two-mechanism bimodality, while accountant-level aggregates are cleanly trimodal (BIC-best $K = 3$), reflecting that individual signing *behavior* is close to discrete even when pixel-level output *quality* is continuous.
+The accountant-level 2-component crossings yield principled thresholds (cosine $= 0.945$, dHash $= 8.10$).
+A major Big-4 firm is used as a *replication-dominated* (not pure) calibration anchor, with interview and visual evidence supporting majority non-hand-signing and a minority of hand-signers.
+Validation against 310 pixel-identical signature pairs and a low-similarity negative anchor yields perfect recall at all candidate thresholds.
+To our knowledge, this represents the largest-scale forensic analysis of auditor signature authenticity reported in the literature.
+
+<!-- Word count: ~290 -->