gbanyan
12f716ddf1
Fully addresses the partial-resolution / unfixed items from codex
gpt-5.4 round-4 review (codex_review_gpt54_v3_4.md):
Critical
- Table XI z/p columns now reproduce from displayed counts. Earlier
table had 1-4-unit transcription errors in k values and a fabricated
cos > 0.9407 calibration row; both fixed by rerunning Script 24
with cos = 0.9407 added to COS_RULES and copying exact values from
the JSON output.
- Section III-L classifier now defined entirely in terms of the
independent-minimum dHash statistic that the deployed code (Scripts
21, 23, 24) actually uses; the legacy "cosine-conditional dHash"
language is removed. Tables IX, XI, XII, XVI are now arithmetically
consistent with the III-L classifier definition.
- "0.95 not calibrated to Firm A" inconsistency reconciled: Section
III-H now correctly says 0.95 is the whole-sample Firm A P95 of the
per-signature cosine distribution, matching III-L and IV-F.
Major
- Abstract trimmed to 246 words (from 367) to meet IEEE Access 250-word
limit. Removed "we break the circularity" overclaim; replaced with
"report capture rates on both folds with Wilson 95% intervals to
make fold-level variance visible".
- Conclusion mirrors the Abstract reframe: 70/30 split documents
within-firm sampling variance, not external generalization.
- Introduction no longer promises precision / F1 / EER metrics that
Methods/Results don't deliver; replaced with anchor-based capture /
FAR + Wilson CI language.
- Section III-G within-auditor-year empirical-check wording corrected:
intra-report consistency (IV-H.3) is a different test (two co-signers
on the same report, firm-level homogeneity) and is not a within-CPA
year-level mixing check; the assumption is maintained as a bounded
identification convention.
- Section III-H "two analyses fully threshold-free" corrected to "only
the partner-level ranking is threshold-free"; longitudinal-stability
uses 0.95 cutoff, intra-report uses the operational classifier.
Minor
- Impact Statement removed from export_v3.py SECTIONS list (IEEE Access
Regular Papers do not have a standalone Impact Statement). The file
itself is retained as an archived non-paper note for cover-letter /
grant-report reuse, with a clear archive header.
- All 7 previously unused references ([27] dHash, [31][32] partner-
signature mandates, [33] Taiwan partner rotation, [34] YOLO original,
[35] VLM survey, [36] Mann-Whitney) are now cited in-text:
[27] in Methodology III-E (dHash definition)
[31][32][33] in Introduction (audit-quality regulation context)
[34][35] in Methodology III-C/III-D
[36] in Results IV-C (Mann-Whitney result)
Updated Script 24 to include cos = 0.9407 in COS_RULES so Table XI's
calibration-fold P5 row is computed from the same data file as the
other rows.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
1.6 KiB
Impact Statement (archived; not in IEEE Access submission)
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 a pipeline that automatically extracts and analyzes signatures from over 90,000 audit reports spanning a decade of filings by publicly listed companies in Taiwan. Combining deep-learning visual features with perceptual hashing and three methodologically distinct threshold-selection methods, the system stratifies signatures into a five-way confidence-graded classification and quantifies how the practice varies across firms and over time. After further validation, the technology could support financial regulators in screening signature authenticity at national scale.