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pdf_signature_extraction/paper/paper_a_references_v3.md
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gbanyan b6913d2f93 Phase 6 round-2 reviewer revisions: §III-H.1 promotion + framing alignment
Structural:
- Promote operational classifier definition from §III-L.0 to new §III-H.1, so
  the reader meets the five-way HC/MC/HSC/UN/LH rule before the §III-I/J/K
  diagnostic chain instead of ~130 lines after. §III-L renamed to
  "Anchor-Based Threshold Calibration"; §III-L.0 retains only calibration
  methodology, three units of analysis, any-pair semantics, and the FAR
  terminological note. §III-L.7 deleted (redundant with §III-J).
- Reorganise §V-H Limitations into Primary / Secondary / Documented features /
  Engineering groupings (was a flat 14-item list).
- Reframe §III-M from "ten-tool unsupervised-validation collection" to
  "each diagnostic addresses one specific unsupervised failure mode";
  rename "What v4.0 does/does not claim" → "Limits / Scope of the present
  analysis"; retitle Table XXVII.

Framing alignment (cross-section):
- Strip all v3.x / v4.0 / v3.20 / v4-new / inherited lineage labels from
  rendered text (Abstract, Intro, §II, §III, §IV, §V, §VI, Appendix, Impact).
- Replace "Paper A" rule references with "deployed" rule references.
- Soften "validation" to "characterise" / "check" / "screening label" /
  "consistency check" / "support"; "verdict" → "screening label".
- Remove codex-verified spike claims (non-Big-4 jittered dHash, Big-4 pooled
  cosine after firm-mean centring). Only formally scripted evidence
  (Scripts 39b–39e) retained; non-Big-4 evidence framed as corroborating
  raw-axis cosine, not as calibration evidence.
- Strip script-provenance parentheticals from Introduction; defer Script 39c
  internal references and similar to Methodology / Appendix.

Numerical / table fixes:
- §III-C document-count arithmetic: 12 corrupted → 13 corrupted/unreadable,
  verified against sqlite DB and total-pdf/ folder counts (90,282 - 4,198
  no-sig - 13 corrupted = 86,071 → 85,042 with detections → 182,328 sigs →
  168,755 CPA-matched). Table I shows VLM-positive (86,084) and
  processed-for-extraction (86,071) as separate rows.
- Wilson 95% CIs added for joint-rule ICCR rows in Table XXI / methodology
  table ([0.00011, 0.00018] and [0.00008, 0.00014]).
- Unit error fixed: 0.3856 pp / 0.4431 pp → 0.3856 (38.6 pp) / 0.4431 (44.3 pp).

Smaller revisions:
- Pipeline framing: "detecting" → "screening" in Abstract / Intro / Conclusion
  for consistency with the unsupervised-screening positioning.
- "hard ground-truth subset" → "conservative hard-positive subset" throughout.
- §III-F SSIM / pixel-comparison rebuttal compressed from ~15 lines to 4;
  design-level argument deferred to supplementary materials.
- "stakeholders can adopt / can derive thresholds" → "alternative operating
  points can be characterised by inverting" (less prescriptive).
- "the same mechanism extending in milder form to Firms B/C/D" → "similar,
  milder production-related reuse patterns at Firms B/C/D" (mechanism claim
  softened).
- Appendix A "non-hand-signed mode" / "two-mechanism mixture" lineage language
  aligned with v4 framing.

Appendix B:
- Rebuilt as a redirect-only stub. The HTML-commented obsolete table mapping
  (Table IX–XVIII labels with FAR / capture-rate / validation language) is
  removed; replaced with a short paragraph pointing to supplementary
  materials for full table-to-script provenance.

Cross-references:
- All §III-L references for the rule definition retargeted to §III-H.1;
  references for calibration still point to §III-L.
- §III-H references for byte-level Firm A evidence / non-Big-4 reverse anchor
  retargeted to §III-H.2.

Artefacts:
- Combined manuscript regenerated: paper_a_v4_combined.md, 1314 lines
  (was 1346 pre-review).
- Two review handoff documents added:
  paper/review_handoff_abstract_intro_20260515.md
  paper/review_handoff_body_20260515.md

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-15 18:07:31 +08:00

7.2 KiB
Raw Blame History

References

[1] Taiwan Certified Public Accountant Act (會計師法), Art. 4; FSC Attestation Regulations (查核簽證核准準則), Art. 6. Available: https://law.moj.gov.tw/ENG/LawClass/LawAll.aspx?pcode=G0400067

[2] S.-H. Yen, Y.-S. Chang, and H.-L. Chen, "Does the signature of a CPA matter? Evidence from Taiwan," Res. Account. Regul., vol. 25, no. 2, pp. 230235, 2013.

[3] J. Bromley et al., "Signature verification using a Siamese time delay neural network," in Proc. NeurIPS, 1993.

[4] S. Dey et al., "SigNet: Convolutional Siamese network for writer independent offline signature verification," arXiv:1707.02131, 2017.

[5] H.-H. Kao and C.-Y. Wen, "An offline signature verification and forgery detection method based on a single known sample and an explainable deep learning approach," Appl. Sci., vol. 10, no. 11, p. 3716, 2020.

[6] H. Li et al., "TransOSV: Offline signature verification with transformers," Pattern Recognit., vol. 145, p. 109882, 2024.

[7] S. Tehsin et al., "Enhancing signature verification using triplet Siamese similarity networks in digital documents," Mathematics, vol. 12, no. 17, p. 2757, 2024.

[8] P. Brimoh and C. C. Olisah, "Consensus-threshold criterion for offline signature verification using CNN learned representations," arXiv:2401.03085, 2024.

[9] N. Woodruff et al., "Fully-automatic pipeline for document signature analysis to detect money laundering activities," arXiv:2107.14091, 2021.

[10] S. Abramova and R. Böhme, "Detecting copy-move forgeries in scanned text documents," in Proc. Electronic Imaging, 2016.

[11] Y. Li et al., "Copy-move forgery detection in digital image forensics: A survey," Multimedia Tools Appl., 2024.

[12] Y. Jakhar and M. D. Borah, "Effective near-duplicate image detection using perceptual hashing and deep learning," Inf. Process. Manage., p. 104086, 2025.

[13] E. Pizzi et al., "A self-supervised descriptor for image copy detection," in Proc. CVPR, 2022.

[14] L. G. Hafemann, R. Sabourin, and L. S. Oliveira, "Learning features for offline handwritten signature verification using deep convolutional neural networks," Pattern Recognit., vol. 70, pp. 163176, 2017.

[15] E. N. Zois, D. Tsourounis, and D. Kalivas, "Similarity distance learning on SPD manifold for writer independent offline signature verification," IEEE Trans. Inf. Forensics Security, vol. 19, pp. 13421356, 2024.

[16] L. G. Hafemann, R. Sabourin, and L. S. Oliveira, "Meta-learning for fast classifier adaptation to new users of signature verification systems," IEEE Trans. Inf. Forensics Security, vol. 15, pp. 17351745, 2020.

[17] H. Farid, "Image forgery detection," IEEE Signal Process. Mag., vol. 26, no. 2, pp. 1625, 2009.

[18] F. Z. Mehrjardi, A. M. Latif, M. S. Zarchi, and R. Sheikhpour, "A survey on deep learning-based image forgery detection," Pattern Recognit., vol. 144, art. no. 109778, 2023.

[19] J. Luo et al., "A survey of perceptual hashing for multimedia," ACM Trans. Multimedia Comput. Commun. Appl., vol. 21, no. 7, 2025.

[20] D. Engin et al., "Offline signature verification on real-world documents," in Proc. CVPRW, 2020.

[21] D. Tsourounis et al., "From text to signatures: Knowledge transfer for efficient deep feature learning in offline signature verification," Expert Syst. Appl., vol. 189, art. 116136, 2022.

[22] B. Chamakh and O. Bounouh, "A unified ResNet18-based approach for offline signature classification and verification across multilingual datasets," Procedia Comput. Sci., vol. 270, pp. 40244033, 2025.

[23] A. Babenko, A. Slesarev, A. Chigorin, and V. Lempitsky, "Neural codes for image retrieval," in Proc. ECCV, 2014, pp. 584599.

[24] S. Bai, K. Chen, X. Liu, J. Wang, W. Ge, S. Song, K. Dang, P. Wang, S. Wang, J. Tang, H. Zhong, Y. Zhu, M. Yang, Z. Li, J. Wan, P. Wang, W. Ding, Z. Fu, Y. Xu, J. Ye, X. Zhang, T. Xie, Z. Cheng, H. Zhang, Z. Yang, H. Xu, and J. Lin, "Qwen2.5-VL technical report," arXiv:2502.13923, 2025. [Online]. Available: https://arxiv.org/abs/2502.13923

[25] Ultralytics, "YOLO11 documentation," 2024. [Online]. Available: https://docs.ultralytics.com/models/yolo11/

[26] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. CVPR, 2016.

[27] N. Krawetz, "Kind of like that," The Hacker Factor Blog, 2013. [Online]. Available: https://www.hackerfactor.com/blog/index.php?/archives/529-Kind-of-Like-That.html

[28] B. W. Silverman, Density Estimation for Statistics and Data Analysis. London: Chapman & Hall, 1986.

[29] J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Lawrence Erlbaum, 1988.

[30] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Trans. Image Process., vol. 13, no. 4, pp. 600612, 2004.

[31] J. V. Carcello and C. Li, "Costs and benefits of requiring an engagement partner signature: Recent experience in the United Kingdom," The Accounting Review, vol. 88, no. 5, pp. 15111546, 2013.

[32] A. D. Blay, M. Notbohm, C. Schelleman, and A. Valencia, "Audit quality effects of an individual audit engagement partner signature mandate," Int. J. Auditing, vol. 18, no. 3, pp. 172192, 2014.

[33] W. Chi, H. Huang, Y. Liao, and H. Xie, "Mandatory audit partner rotation, audit quality, and market perception: Evidence from Taiwan," Contemp. Account. Res., vol. 26, no. 2, pp. 359391, 2009.

[34] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proc. CVPR, 2016, pp. 779788.

[35] J. Zhang, J. Huang, S. Jin, and S. Lu, "Vision-language models for vision tasks: A survey," IEEE Trans. Pattern Anal. Mach. Intell., vol. 46, no. 8, pp. 56255644, 2024.

[36] H. B. Mann and D. R. Whitney, "On a test of whether one of two random variables is stochastically larger than the other," Ann. Math. Statist., vol. 18, no. 1, pp. 5060, 1947.

[37] J. A. Hartigan and P. M. Hartigan, "The dip test of unimodality," Ann. Statist., vol. 13, no. 1, pp. 7084, 1985.

[38] D. Burgstahler and I. Dichev, "Earnings management to avoid earnings decreases and losses," J. Account. Econ., vol. 24, no. 1, pp. 99126, 1997.

[39] J. McCrary, "Manipulation of the running variable in the regression discontinuity design: A density test," J. Econometrics, vol. 142, no. 2, pp. 698714, 2008.

[40] A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Statist. Soc. B, vol. 39, no. 1, pp. 138, 1977.

[41] H. White, "Maximum likelihood estimation of misspecified models," Econometrica, vol. 50, no. 1, pp. 125, 1982.

[42] M. Stone, "Cross-validatory choice and assessment of statistical predictions," J. R. Statist. Soc. B, vol. 36, no. 2, pp. 111147, 1974.

[43] S. Geisser, "The predictive sample reuse method with applications," J. Amer. Statist. Assoc., vol. 70, no. 350, pp. 320328, 1975.

[44] A. Vehtari, A. Gelman, and J. Gabry, "Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC," Stat. Comput., vol. 27, no. 5, pp. 14131432, 2017.