gbanyan
0ff1845b22
Three blockers from codex gpt-5.4 round-3 review (codex_review_gpt54_v3_3.md):
B1 Classifier vs three-method threshold mismatch
- Methodology III-L rewritten to make explicit that the per-signature
classifier and the accountant-level three-method convergence operate
at different units (signature vs accountant) and are complementary
rather than substitutable.
- Add Results IV-G.3 + Table XII operational-threshold sensitivity:
cos>0.95 vs cos>0.945 shifts dual-rule capture by 1.19 pp on whole
Firm A; ~5% of signatures flip at the Uncertain/Moderate boundary.
B2 Held-out validation false "within Wilson CI" claim
- Script 24 recomputes both calibration-fold and held-out-fold rates
with Wilson 95% CIs and a two-proportion z-test on each rule.
- Table XI replaced with the proper fold-vs-fold comparison; prose
in Results IV-G.2 and Discussion V-C corrected: extreme rules agree
across folds (p>0.7); operational rules in the 85-95% band differ
by 1-5 pp due to within-Firm-A heterogeneity (random 30% sample
contained more high-replication C1 accountants), not generalization
failure.
B3 Interview evidence reframed as practitioner knowledge
- The Firm A "interviews" referenced throughout v3.3 are private,
informal professional conversations, not structured research
interviews. Reframed accordingly: all "interview*" references in
abstract / intro / methodology / results / discussion / conclusion
are replaced with "domain knowledge / industry-practice knowledge".
- This avoids overclaiming methodological formality and removes the
human-subjects research framing that triggered the ethics-statement
requirement.
- Section III-H four-pillar Firm A validation now stands on visual
inspection, signature-level statistics, accountant-level GMM, and
the three Section IV-H analyses, with practitioner knowledge as
background context only.
- New Section III-M ("Data Source and Firm Anonymization") covers
MOPS public-data provenance, Firm A/B/C/D pseudonymization, and
conflict-of-interest declaration.
Add signature_analysis/24_validation_recalibration.py for the recomputed
calib-vs-held-out z-tests and the classifier sensitivity analysis;
output in reports/validation_recalibration/.
Pending (not in this commit): abstract length (368 -> 250 words),
Impact Statement removal, BD/McCrary sensitivity reporting, full
reproducibility appendix, references cleanup.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
17 lines
2.8 KiB
Markdown
17 lines
2.8 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 methodologically distinct methods---Kernel Density antimode with a Hartigan unimodality 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 methods reveal an informative asymmetry: signature-level similarity forms a continuous quality spectrum that no two-component mixture cleanly separates, while accountant-level aggregates are clustered into three recognizable groups (BIC-best $K = 3$) with the KDE antimode and the two mixture-based estimators converging within $\sim$0.006 of each other at cosine $\approx 0.975$; the Burgstahler-Dichev / McCrary test produces no significant discontinuity at the accountant level, consistent with clustered-but-smooth rather than sharply discrete accountant-level heterogeneity.
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A major Big-4 firm is used as a *replication-dominated* (not pure) calibration anchor, with visual-inspection and accountant-level mixture evidence supporting majority non-hand-signing and a minority of hand-signers; we break the circularity of using the same firm for calibration and validation by a 70/30 CPA-level held-out fold.
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Validation against 310 byte-identical positive signatures and a $\sim$50,000-pair inter-CPA negative anchor yields FAR $\leq$ 0.001 with Wilson 95% confidence intervals at all accountant-level 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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