# Abstract

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Regulations in many jurisdictions require Certified Public Accountants (CPAs) to attest to each audit report they certify, typically by affixing a signature or seal.
However, the digitization of financial reporting makes it straightforward to reuse a scanned signature image across multiple reports, potentially undermining the intent of individualized attestation.
Unlike signature forgery, where an impostor imitates another person's handwriting, signature replication involves a legitimate signer reusing a digital copy of their own genuine signature---a practice that is difficult to detect through manual inspection at scale.
We present an end-to-end AI pipeline that automatically detects signature replication in financial audit reports.
The pipeline employs a Vision-Language Model for signature page identification, YOLOv11 for signature region detection, and ResNet-50 for deep feature extraction, followed by a dual-method verification combining cosine similarity with difference hashing (dHash).
This dual-method design distinguishes consistent handwriting style (high feature similarity but divergent perceptual hashes) from digital replication (convergent evidence across both methods), addressing an ambiguity that single-metric approaches cannot resolve.
We apply this pipeline to 90,282 audit reports filed by publicly listed companies in Taiwan over a decade (2013--2023), analyzing 182,328 signatures from 758 CPAs.
Using an accounting firm independently identified as employing digital replication as a calibration reference, we establish empirically grounded detection thresholds.
Our analysis reveals that among documents with high feature-level similarity (cosine > 0.95), the structural verification layer stratifies them into distinct populations: 41% with converging replication evidence, 52% with partial structural similarity, and 7% with no structural corroboration despite near-identical features---demonstrating that single-metric approaches conflate style consistency with digital duplication.
To our knowledge, this represents the largest-scale analysis of signature authenticity in financial audit documents to date.

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