gbanyan
9d19ca5a31
Major fixes per codex (gpt-5.4) review: ## Structural fixes - Fixed three-method convergence overclaim: added Script 20 to run KDE antimode, BD/McCrary, and Beta mixture EM on accountant-level means. Accountant-level 1D convergence: KDE antimode=0.973, Beta-2=0.979, LogGMM-2=0.976 (within ~0.006). BD/McCrary finds no transition at accountant level (consistent with smooth clustering, not sharp discontinuity). - Disambiguated Method 1: KDE crossover (between two labeled distributions, used at signature all-pairs level) vs KDE antimode (single-distribution local minimum, used at accountant level). - Addressed Firm A circular validation: Script 21 adds CPA-level 70/30 held-out fold. Calibration thresholds derived from 70% only; heldout rates reported with Wilson 95% CIs (e.g. cos>0.95 heldout=93.61% [93.21%-93.98%]). - Fixed 139+32 vs 180: the split is 139/32 of 171 Firm A CPAs with >=10 signatures (9 CPAs excluded for insufficient sample). Reconciled across intro, results, discussion, conclusion. - Added document-level classification aggregation rule (worst-case signature label determines document label). ## Pixel-identity validation strengthened - Script 21: built ~50,000-pair inter-CPA random negative anchor (replaces the original n=35 same-CPA low-similarity negative which had untenable Wilson CIs). - Added Wilson 95% CI for every FAR in Table X. - Proper EER interpolation (FAR=FRR point) in Table X. - Softened "conservative recall" claim to "non-generalizable subset" language per codex feedback (byte-identical positives are a subset, not a representative positive class). - Added inter-CPA stats: mean=0.762, P95=0.884, P99=0.913. ## Terminology & sentence-level fixes - "statistically independent methods" -> "methodologically distinct methods" throughout (three diagnostics on the same sample are not independent). - "formal bimodality check" -> "unimodality test" (dip test tests H0 of unimodality; rejection is consistent with but not a direct test of bimodality). - "Firm A near-universally non-hand-signed" -> already corrected to "replication-dominated" in prior commit; this commit strengthens that framing with explicit held-out validation. - "discrete-behavior regimes" -> "clustered accountant-level heterogeneity" (BD/McCrary non-transition at accountant level rules out sharp discrete boundaries; the defensible claim is clustered-but-smooth). - Softened White 1982 quasi-MLE claim (no longer framed as a guarantee). - Fixed VLM 1.2% FP overclaim (now acknowledges the 1.2% could be VLM FP or YOLO FN). - Unified "310 byte-identical signatures" language across Abstract, Results, Discussion (previously alternated between pairs/signatures). - Defined min_dhash_independent explicitly in Section III-G. - Fixed table numbering (Table XI heldout added, classification moved to XII, ablation to XIII). - Explained 84,386 vs 85,042 gap (656 docs have only one signature, no pairwise stat). - Made Table IX explicitly a "consistency check" not "validation"; paired it with Table XI held-out rates as the genuine external check. - Defined 0.941 threshold (calibration-fold Firm A cosine P5). - Computed 0.945 Firm A rate exactly (94.52%) instead of interpolated. - Fixed Ref [24] Qwen2.5-VL to full IEEE format (arXiv:2502.13923). ## New artifacts - Script 20: accountant-level three-method threshold analysis - Script 21: expanded validation (inter-CPA anchor, held-out Firm A 70/30) - paper/codex_review_gpt54_v3.md: preserved review feedback Output: Paper_A_IEEE_Access_Draft_v3.docx (391 KB, rebuilt from v3.1 markdown sources). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
1.1 KiB
Impact Statement
Auditor signatures on financial reports are a key safeguard of corporate accountability. When the signature on an audit report is produced by reproducing a stored image instead of by the partner's own hand---whether through an administrative stamping workflow or a firm-level electronic signing system---this safeguard is weakened, yet detecting the practice through manual inspection is infeasible at the scale of modern financial markets. We developed an artificial intelligence system that automatically extracts and analyzes signatures from over 90,000 audit reports spanning a decade of filings by publicly listed companies in Taiwan. By combining deep-learning visual features with perceptual hashing and three methodologically distinct threshold-selection methods, the system distinguishes genuinely hand-signed signatures from reproduced ones and quantifies how this practice varies across firms and over time. After further validation, the technology could support financial regulators in automating signature-authenticity screening at national scale.