gbanyan
d2f8673a67
Rewrites Section III-G (Unit of Analysis and Summary Statistics) after self-review identified three logical issues in v3.10: 1. Ordering inversion: the three units are now ordered signature -> auditor-year -> accountant, with auditor-year as the principled middle unit under within-year assumptions and accountant as a deliberate cross-year pooling. 2. Oversold assumption: the old "within-auditor-year no-mixing identification assumption" is split into A1 (pair-detectability, weak statistical, cross-year scope matching the detector) and A2 (within-year label uniformity, interpretive convention). The arithmetic statistics reported in the paper do not require A2; A2 only underwrites interpretive readings (notably IV-H.1's partner- level "minority of hand-signers" framing). 3. Motivation-assumption mismatch: removed the "longitudinal behaviour of interest" framing and explicitly disclaimed across-year homogeneity. Accountant-level coordinates are now described as a pooled observed tendency rather than a time-invariant regime. Propagated implications across Introduction, Discussion, and Results: softened "tends to cluster into a dominant regime" and "directly quantifying the minority of hand-signers" to "pooled observed tendency" / "consistent with within-firm heterogeneity"; rewrote the Limitations fifth point (was "treats all signatures from a CPA as a single class"); added a seventh Limitation acknowledging the source-template edge case; added a per-signature best-match cross-year caveat to Section IV-H.2; softened IV-H.2's "direct consequence" to "consistent with"; reframed pixel-identity anchor as pair-level proof of image reuse (with source-template exception) rather than absolute signature-level positive. Process: self-review (9 findings) -> full-pass fixes -> codex gpt-5.5 xhigh round-10 verification (8 RESOLVED, 1 PARTIAL, 4 MINOR regression findings) -> regression fixes. No re-computation. All tables (IV-XVIII) and Appendix A numbers unchanged. Abstract at 248/250 words. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2.1 KiB
Abstract
Regulations require Certified Public Accountants (CPAs) to attest to each audit report by affixing a signature. Digitization makes reusing a stored signature image across reports trivial---through administrative stamping or firm-level electronic signing---potentially undermining individualized attestation. Unlike forgery, non-hand-signed reproduction reuses the legitimate signer's own stored image, making it visually invisible to report users and infeasible to audit at scale manually. We present a pipeline integrating a Vision-Language Model for signature-page identification, YOLOv11 for signature detection, and ResNet-50 for feature extraction, followed by dual-descriptor verification combining cosine similarity and difference hashing. For threshold determination we apply two estimators---kernel-density antimode with a Hartigan unimodality test and an EM-fitted Beta mixture with a logit-Gaussian robustness check---plus a Burgstahler-Dichev/McCrary density-smoothness diagnostic, at the signature and accountant levels. Applied to 90,282 audit reports filed in Taiwan over 2013-2023 (182,328 signatures from 758 CPAs), the methods reveal a level asymmetry: signature-level similarity is a continuous quality spectrum that no two-component mixture separates, while accountant-level aggregates cluster into three groups with the antimode and two mixture estimators converging within $\sim$0.006 at cosine \approx 0.975. 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 alongside within-firm heterogeneity consistent with a minority of hand-signers; capture rates on both 70/30 calibration and held-out folds are reported with Wilson 95% intervals to make fold-level variance visible. Validation against 310 byte-identical positives and a $\sim$50,000-pair inter-CPA negative anchor yields FAR \leq 0.001 at all accountant-level thresholds.