<!--
ARCHIVED. Not part of the IEEE Access submission.

IEEE Access Regular Papers do not include a separate Impact Statement
section. The text below is retained for possible reuse in a cover
letter, grant report, or non-IEEE venue. It is excluded from the
assembled paper by export_v3.py.

If reused, note that the wording "distinguishes genuinely hand-signed
signatures from reproduced ones" overstates what a five-way confidence
classifier without a fully labeled test set establishes; soften before
external use.
-->

# 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 two methodologically distinct threshold estimators (plus a density-smoothness diagnostic), 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.
