gbanyan
3672c9343e
Framing softening (per partner tone decision: own the limitation rather than defend the strong claim). Abstract: "Firm heterogeneity is decisive ... consistent with firm-level template-like reuse" -> "The framework surfaces pronounced firm-level heterogeneity ... consistent with firm-level template-like reuse but not independently diagnostic, since descriptor-only data cannot separate reuse from digitisation-pipeline or signing-style homogeneity within a firm; we report it as a scope limitation rather than a mechanism finding." S V-H Limitations: new bullet "Mechanism attribution for the firm-level heterogeneity is not identifiable from descriptor-only data." enumerates three non-mutually-exclusive firm-level mechanisms (template-like reuse / digitisation-pipeline homogeneity / signing-style homogeneity), notes the (cosine, dHash) descriptor pair is by construction indifferent to which mechanism generated a near-identical pair, and lists what additional data would be needed for attribution. S VI Conclusion items (3) and (4): "firm heterogeneity quantification" -> "firm-level heterogeneity surfaced by the framework ... reported as a framework-discriminative observation rather than a mechanism finding"; item (4) expanded from template/stamp/document-production reuse alone to the three-mechanism scope, with explicit "not independently establishing" and S V-H cross-reference. DOCX export fix (export_v3.py): add missing LaTeX-to-Unicode tokens (\checkmark, \lvert/\rvert, \lVert/\rVert, \in, \notin, \max, \min, \log, \ln, \exp, \bullet) that were silently dropping content from Table III rows 2-4 (integer-jitter robustness check marks empty) and Table XVIII drift column (|Delta| empty). Rebuild Paper_A_IEEE_Access_Draft_v3.docx via export_v3.py and install copy as Paper_A_IEEE_Access_Draft_v4.0_20260515.docx (replaces prior pandoc-built v4 DOCX which had empty cells in every table header with LaTeX math and inconsistent column widths). All 43 tables now have non-empty cells with sub/superscript runs. Mirrored in paper_a_v4_combined.md for consistency with the single-file combined source. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Abstract
Regulations require Certified Public Accountants (CPAs) to attest each audit report with a signature, but digitization makes it feasible to reuse a stored signature image across reports — through administrative stamping or firm-level electronic signing — thereby undermining individualized attestation. We build an end-to-end pipeline for screening such non-hand-signed signatures at scale: a Vision-Language Model identifies signature pages, YOLOv11 localizes signatures, ResNet-50 supplies deep features, and a dual-descriptor layer combines cosine similarity with an independent-minimum perceptual hash (dHash) to separate style consistency from image reproduction. Applied to 90,282 Taiwan audit reports (2013–2023), the pipeline yields 182,328 signatures from 758 CPAs; primary analyses are scoped to the Big-4 sub-corpus (437 CPAs; 150,442 signatures). Distributional diagnostics show that the apparent multimodality of the descriptor distribution dissolves under joint firm-mean centring and integer-tie jitter (p rises to 0.35), so no within-population bimodal antimode anchors the operational thresholds. We instead adopt an anchor-based inter-CPA coincidence-rate (ICCR) calibration at three units: per-comparison (0.0006 at cos$>0.95$; 0.0013 at dHash$\leq 5$; 0.00014 jointly), pool-normalised per-signature (0.11 under the deployed any-pair high-confidence rule), and per-document (0.34 for the operational HC+MC alarm). The framework surfaces pronounced firm-level heterogeneity: Firm A's per-document HC+MC ICCR is 0.62 versus $0.09$–0.16 at Firms B/C/D (gap persists after pool-size adjustment), and $77$–99\% of inter-CPA collisions concentrate within the source firm. This contrast is consistent with firm-level template-like reuse but not independently diagnostic, since descriptor-only data cannot separate reuse from digitisation-pipeline or signing-style homogeneity within a firm; we report it as a scope limitation rather than a mechanism finding. We position the system as a specificity-proxy-anchored screening framework with human-in-the-loop review, not as a validated forensic detector; no calibrated error rates are reportable without signature-level ground truth.