gbanyan/pdf_signature_extraction
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Paper A v3.2: partner v4 feedback integration (threshold-independent benchmark validation)

Browse Source 
Partner v4 (signature_paper_draft_v4) proposed 3 substantive improvements;
partner confirmed the 2013-2019 restriction was an error (sample stays
2013-2023). The remaining suggestions are adopted with our own data.

## New scripts
- Script 22 (partner ranking): ranks all Big-4 auditor-years by mean
  max-cosine. Firm A occupies 95.9% of top-10% (base 27.8%), 3.5x
  concentration ratio. Stable across 2013-2023 (88-100% per year).
- Script 23 (intra-report consistency): for each 2-signer report,
  classify both signatures and check agreement. Firm A agrees 89.9%
  vs 62-67% at other Big-4. 87.5% Firm A reports have BOTH signers
  non-hand-signed; only 4 reports (0.01%) both hand-signed.

## New methodology additions
- III-G: explicit within-auditor-year no-mixing identification
  assumption (supported by Firm A interview evidence).
- III-H: 4th Firm A validation line: threshold-independent evidence
  from partner ranking + intra-report consistency.

## New results section IV-H (threshold-independent validation)
- IV-H.1: Firm A year-by-year cosine<0.95 rate. 2013-2019 mean=8.26%,
  2020-2023 mean=6.96%, 2023 lowest (3.75%). Stability contradicts
  partner's hypothesis that 2020+ electronic systems increase
  heterogeneity -- data shows opposite (electronic systems more
  consistent than physical stamping).
- IV-H.2: partner ranking top-K tables (pooled + year-by-year).
- IV-H.3: intra-report consistency per-firm table.

## Renumbering
- Section H (was Classification Results) -> I
- Section I (was Ablation) -> J
- Tables XIII-XVI new (yearly stability, top-K pooled, top-10% per-year,
  intra-report), XVII = classification (was XII), XVIII = ablation
  (was XIII).

These threshold-independent analyses address the codex review concern
about circular validation by providing benchmark evidence that does not
depend on any threshold calibrated to Firm A itself.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
gbanyan
2026-04-21 01:59:49 +08:00
parent 9d19ca5a31
commit 51d15b32a5
5 changed files with 677 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files
paper/Paper_A_IEEE_Access_Draft_v3.docx
BIN
View File
Binary file not shown.
paper/paper_a_methodology_v3.md
+12 -1
View File
@@ -120,6 +120,12 @@ For per-signature classification we compute, for each signature, the maximum pai
The max/min (rather than mean) formulation reflects the identification logic for non-hand-signing: if even one other signature of the same CPA is a pixel-level reproduction, that pair will dominate the extremes and reveal the non-hand-signed mechanism.
Mean statistics would dilute this signal.
We also adopt an explicit *within-auditor-year no-mixing* identification assumption.
Specifically, within any single fiscal year we treat a given CPA's signing mechanism as uniform: a CPA who reproduces one signature image in that year is assumed to do so for every report, and a CPA who hand-signs in that year is assumed to hand-sign every report in that year.
Interview evidence from Firm A partners supports this assumption for their firm during the sample period.
Under the assumption, per-auditor-year summary statistics are well defined and robust to outliers: if even one pair of same-CPA signatures in the year is near-identical, the max/min captures it.
The intra-report consistency analysis in Section IV-H.3 provides an empirical check on the within-auditor-year assumption at the report level.
For accountant-level analysis we additionally aggregate these per-signature statistics to the CPA level by computing the mean best-match cosine and the mean *independent minimum dHash* across all signatures of that CPA.
The *independent minimum dHash* of a signature is defined as the minimum Hamming distance to *any* other signature of the same CPA (over the full same-CPA set), in contrast to the *cosine-conditional dHash* used as a diagnostic elsewhere, which is the dHash to the single signature selected as the cosine-nearest match.
The independent minimum avoids conditioning on the cosine choice and is therefore the conservative structural-similarity statistic for each signature.
@@ -137,7 +143,12 @@ Crucially, the same interview evidence does *not* exclude the possibility that a
Second, independent visual inspection of randomly sampled Firm A reports reveals pixel-identical signature images across different audit engagements and fiscal years for the majority of partners.
Third, our own quantitative analysis is consistent with the above: 92.5% of Firm A's per-signature best-match cosine similarities exceed 0.95, consistent with non-hand-signing as the dominant mechanism, while the remaining 7.5% exhibit lower best-match values consistent with the minority of hand-signers identified in the interviews.
We emphasize that this 92.5% figure is a within-sample consistency check rather than an independent validation of Firm A's status; the validation role is played by the interview and visual-inspection evidence enumerated above and by the held-out Firm A fold described in Section III-K.
Fourth, we additionally validate the Firm A benchmark through two analyses that do not depend on any threshold we subsequently calibrate:
(a) *Partner-level similarity ranking (Section IV-H.2).* When every Big-4 auditor-year is ranked globally by its per-auditor-year mean best-match cosine, Firm A auditor-years account for 95.9% of the top decile against a baseline share of 27.8% (a 3.5$\times$ concentration ratio), and this over-representation is stable across 2013-2023.
(b) *Intra-report consistency (Section IV-H.3).* Because each Taiwanese statutory audit report is co-signed by two engagement partners, firmwide stamping practice predicts that both signers on a given Firm A report should receive the same signature-level label. Firm A exhibits 89.9% intra-report agreement against 62-67% at the other Big-4 firms, consistent with firm-wide rather than partner-specific practice.
We emphasize that the 92.5% figure is a within-sample consistency check rather than an independent validation of Firm A's status; the validation role is played by the interview and visual-inspection evidence, by the two threshold-independent analyses above, and by the held-out Firm A fold described in Section III-K.
We emphasize that Firm A's replication-dominated status was *not* derived from the thresholds we calibrate against it.
Its identification rests on domain knowledge and visual evidence that is independent of the statistical pipeline.
paper/paper_a_results_v3.md
+104 -6
View File
@@ -242,12 +242,110 @@ The dual rule cosine $> 0.95$ AND dHash $\leq 8$ captures 91.54% [91.09%, 91.97%
A 30-signature stratified visual sanity sample (six signatures each from pixel-identical, high-cos/low-dh, borderline, style-only, and likely-genuine strata) produced inter-rater agreement with the classifier in all 30 cases; this sample contributed only to spot-check and is not used to compute reported metrics.
## H. Classification Results
## H. Firm A Benchmark Validation: Threshold-Independent Evidence
Table XII presents the final classification results under the dual-descriptor framework with Firm A-calibrated thresholds for 84,386 documents.
The capture rates of Section IV-F are a within-sample consistency check: they evaluate how well a threshold captures Firm A, but the thresholds themselves are anchored to Firm A's percentiles.
This section reports three additional analyses that are *threshold-independent* in the sense that their findings do not depend on any cutoff we calibrate to Firm A, and therefore constitute genuine benchmark-validation evidence rather than a circular check.
### 1) Year-by-Year Stability of the Firm A Left Tail
Table XIII reports the proportion of Firm A signatures with per-signature best-match cosine below 0.95, disaggregated by fiscal year.
Under the replication-dominated interpretation (Section III-H) this left-tail share captures the minority of Firm A partners who continue to hand-sign.
Under the alternative hypothesis that the left tail is an artifact of scan or compression noise, the share should shrink as scanning and PDF-compression technology improved over 2013-2023.
<!-- TABLE XIII: Firm A Per-Year Cosine Distribution
| Year | N sigs | mean cosine | % below 0.95 |
|------|--------|-------------|--------------|
| 2013 | 2,167 | 0.9733 | 12.78% |
| 2014 | 5,256 | 0.9781 | 8.69% |
| 2015 | 5,484 | 0.9793 | 7.46% |
| 2016 | 5,739 | 0.9811 | 6.92% |
| 2017 | 5,796 | 0.9814 | 6.69% |
| 2018 | 5,986 | 0.9808 | 6.58% |
| 2019 | 6,122 | 0.9780 | 8.71% |
| 2020 | 6,122 | 0.9770 | 9.46% |
| 2021 | 5,996 | 0.9792 | 8.37% |
| 2022 | 5,918 | 0.9819 | 6.25% |
| 2023 | 5,862 | 0.9860 | 3.75% |
-->
The left tail is stable at 6-13% throughout the sample period and shows no pre/post-2020 level shift: the 2013-2019 mean left-tail share is 8.26% and the 2020-2023 mean is 6.96%.
The lowest observed share is in 2023 (3.75%), consistent with firm-level electronic signing systems producing more uniform output than earlier manual scanning-and-stamping, not less.
This stability supports the replication-dominated framing: a persistent minority of hand-signing Firm A partners is consistent with a Beta left tail that is stable across production technologies, whereas a noise-only explanation would predict a shrinking share as technology improved.
### 2) Partner-Level Similarity Ranking
If Firm A applies firm-wide stamping while the other Big-4 firms use stamping only for a subset of partners, Firm A auditor-years should disproportionately occupy the top of the similarity distribution among all Big-4 auditor-years.
We test this prediction directly.
For each auditor-year (CPA $\times$ fiscal year) with at least 5 signatures we compute the mean best-match cosine similarity across the year's signatures, yielding 4,629 auditor-years across 2013-2023.
Firm A accounts for 1,287 of these (27.8% baseline share).
Table XIV reports per-firm occupancy of the top $K\%$ of the ranked distribution.
<!-- TABLE XIV: Top-K Similarity Rank Occupancy by Firm (pooled 2013-2023)
| Top-K | k in bucket | Deloitte (Firm A) | KPMG | PwC | EY | Other/Non-Big-4 | Deloitte share |
|-------|-------------|-------------------|------|-----|----|----|-----------------|
| 10% | 462 | 443 | 2 | 3 | 0 | 14 | 95.9% |
| 25% | 1,157 | 1,043 | 32 | 23 | 9 | 50 | 90.1% |
| 50% | 2,314 | 1,220 | 473 | 273 | 102| 246| 52.7% |
-->
Firm A occupies 95.9% of the top 10% and 90.1% of the top 25% of auditor-years by similarity, against its baseline share of 27.8%---a concentration ratio of 3.5$\times$ at the top decile and 3.2$\times$ at the top quartile.
Year-by-year (Table XV), the top-10% Deloitte share ranges from 88.4% (2020) to 100% (2013, 2014, 2017, 2018, 2019), showing that the concentration is stable across the sample period.
<!-- TABLE XV: Deloitte Share of Top-10% Similarity by Year
| Year | N auditor-years | Top-10% k | Deloitte in top-10% | Deloitte share | Deloitte baseline |
|------|-----------------|-----------|---------------------|----------------|-------------------|
| 2013 | 324 | 32 | 32 | 100.0% | 26.2% |
| 2014 | 399 | 39 | 39 | 100.0% | 27.1% |
| 2015 | 394 | 39 | 38 | 97.4% | 27.2% |
| 2016 | 413 | 41 | 39 | 95.1% | 27.4% |
| 2017 | 415 | 41 | 41 | 100.0% | 27.9% |
| 2018 | 434 | 43 | 43 | 100.0% | 28.1% |
| 2019 | 429 | 42 | 42 | 100.0% | 28.2% |
| 2020 | 430 | 43 | 38 | 88.4% | 28.3% |
| 2021 | 450 | 45 | 44 | 97.8% | 28.4% |
| 2022 | 467 | 46 | 43 | 93.5% | 28.5% |
| 2023 | 474 | 47 | 46 | 97.9% | 28.5% |
-->
This over-representation is a direct consequence of firm-wide stamping practice and is not derived from any threshold we subsequently calibrate.
It therefore constitutes genuine cross-firm evidence for Firm A's benchmark status.
### 3) Intra-Report Consistency
Taiwanese statutory audit reports are co-signed by two engagement partners (a primary and a secondary signer).
Under firm-wide stamping practice at a given firm, both signers on the same report should receive the same signature-level classification.
Disagreement between the two signers on a report is informative about whether the stamping practice is firm-wide or partner-specific.
For each report with exactly two signatures and complete per-signature data (93,979 reports), we classify each signature using the dual-descriptor rules of Section III-L and record whether the two classifications agree.
Table XVI reports per-firm intra-report agreement.
<!-- TABLE XVI: Intra-Report Classification Agreement by Firm
| Firm | Total 2-signer reports | Both non-hand-signed | Both uncertain | Both style | Both hand-signed | Mixed | Agreement rate |
|------|-----------------------|----------------------|----------------|------------|------------------|-------|----------------|
| Deloitte (Firm A) | 30,222 | 26,435 | 734 | 0 | 4 | 3,049 | **89.91%** |
| KPMG | 17,121 | 9,260 | 2,159| 5 | 6 | 5,691 | 66.76% |
| PwC | 19,112 | 8,983 | 3,035| 3 | 5 | 7,086 | 62.92% |
| EY | 8,375 | 3,028 | 2,376| 0 | 3 | 2,968 | 64.56% |
| Other / Non-Big-4 | 9,140 | 1,671 | 3,945| 18| 27| 3,479 | 61.94% |
A report is "in agreement" if both signature labels fall in the same coarse bucket
(non-hand-signed = high+moderate; uncertain; style consistency; or likely hand-signed).
-->
Firm A achieves 89.9% intra-report agreement, with 87.5% of Firm A reports having *both* signers classified as non-hand-signed and only 4 reports (0.01%) having both classified as likely hand-signed.
The other Big-4 firms and non-Big-4 firms cluster at 62-67% agreement, a 23-28 percentage-point gap.
This sharp discontinuity in intra-report agreement between Firm A and the other firms is the pattern predicted by firm-wide (rather than partner-specific) stamping practice.
Like the partner-level ranking, this test does not depend on any threshold we calibrate to Firm A; the firm-vs-firm comparison is invariant to the absolute cutoff so long as the cutoff is applied uniformly.
## I. Classification Results
Table XVII presents the final classification results under the dual-descriptor framework with Firm A-calibrated thresholds for 84,386 documents.
The document count (84,386) differs from the 85,042 documents with any YOLO detection (Table III) because 656 documents carry only a single detected signature, for which no same-CPA pairwise comparison and therefore no best-match cosine / min dHash statistic is available; those documents are excluded from the classification reported here.
<!-- TABLE XII: Document-Level Classification (Dual-Descriptor: Cosine + dHash)
<!-- TABLE XVII: Document-Level Classification (Dual-Descriptor: Cosine + dHash)
| Verdict | N (PDFs) | % | Firm A | Firm A % |
|---------|----------|---|--------|----------|
| High-confidence non-hand-signed | 29,529 | 35.0% | 22,970 | 76.0% |
@@ -277,13 +375,13 @@ We note that because the non-hand-signed thresholds are themselves calibrated to
Among non-Firm-A CPAs with cosine $> 0.95$, only 11.3% exhibit dHash $\leq 5$, compared to 58.7% for Firm A---a five-fold difference that demonstrates the discriminative power of the structural verification layer.
This is consistent with the three-method thresholds (Section IV-E, Table VIII) and with the cross-firm compositional pattern of the accountant-level GMM (Table VII).
## I. Ablation Study: Feature Backbone Comparison
## J. Ablation Study: Feature Backbone Comparison
To validate the choice of ResNet-50 as the feature extraction backbone, we conducted an ablation study comparing three pre-trained architectures: ResNet-50 (2048-dim), VGG-16 (4096-dim), and EfficientNet-B0 (1280-dim).
All models used ImageNet pre-trained weights without fine-tuning, with identical preprocessing and L2 normalization.
Table XIII presents the comparison.
Table XVIII presents the comparison.
<!-- TABLE XIII: Backbone Comparison
<!-- TABLE XVIII: Backbone Comparison
| Metric | ResNet-50 | VGG-16 | EfficientNet-B0 |
|--------|-----------|--------|-----------------|
| Feature dim | 2048 | 4096 | 1280 |