gbanyan
9b11f03548
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>
18 lines
2.5 KiB
Markdown
18 lines
2.5 KiB
Markdown
# Abstract
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Regulations in many jurisdictions require Certified Public Accountants (CPAs) to attest to each audit report they certify, typically by affixing a signature or seal.
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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.
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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.
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We present an end-to-end AI pipeline that automatically detects non-hand-signed auditor signatures in financial audit reports.
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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).
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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.
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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.
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The accountant-level 2-component crossings yield principled thresholds (cosine $= 0.945$, dHash $= 8.10$).
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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.
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Validation against 310 pixel-identical signature pairs and a low-similarity negative anchor yields perfect recall at all candidate thresholds.
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To our knowledge, this represents the largest-scale forensic analysis of auditor signature authenticity reported in the literature.
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