# II. Related Work

## A. Offline Signature Verification

Offline signature verification---determining whether a static signature image is genuine or forged---has been studied extensively using deep learning.
Bromley et al. [3] introduced the Siamese neural network architecture for signature verification, establishing the pairwise comparison paradigm that remains dominant.
Hafemann et al. [20] demonstrated that deep CNN features learned from signature images provide strong discriminative representations for writer-independent verification, establishing the foundational baseline for subsequent work.
Dey et al. [4] proposed SigNet, a convolutional Siamese network for writer-independent offline verification, extending this paradigm to generalize across signers without per-writer retraining.
Hadjadj et al. [5] addressed the practical constraint of limited reference samples, achieving competitive verification accuracy using only a single known genuine signature per writer.
More recently, Li et al. [6] introduced TransOSV, the first Vision Transformer-based approach, achieving state-of-the-art results.
Tehsin et al. [7] evaluated distance metrics for triplet Siamese networks, finding that Manhattan distance outperformed cosine and Euclidean alternatives.
Zois et al. [21] proposed similarity distance learning on SPD manifolds for writer-independent verification, achieving robust cross-dataset transfer---a property relevant to our setting where CPA signatures span diverse writing styles.
Hafemann et al. [16] further addressed the practical challenge of adapting to new users through meta-learning, reducing the enrollment burden for signature verification systems.

A common thread in this literature is the assumption that the primary threat is *identity fraud*: a forger attempting to produce a convincing imitation of another person's signature.
Our work addresses a fundamentally different problem---detecting whether the *legitimate signer* reused a digital copy of their own signature---which requires analyzing intra-signer similarity distributions rather than modeling inter-signer discriminability.

Brimoh and Olisah [8] proposed a consensus-threshold approach that derives classification boundaries from known genuine reference pairs, the methodology most closely related to our calibration strategy.
However, their method operates on standard verification benchmarks with laboratory-collected signatures, whereas our approach applies threshold calibration using a known-replication subpopulation identified through domain expertise in real-world regulatory documents.

## B. Document Forensics and Copy Detection

Image forensics encompasses a broad range of techniques for detecting manipulated visual content [17], with recent surveys highlighting the growing role of deep learning in forgery detection [18].
Copy-move forgery detection (CMFD) identifies duplicated regions within or across images, typically targeting manipulated photographs [11].
Abramova and Bohme [10] adapted block-based CMFD to scanned text documents, noting that standard methods perform poorly in this domain because legitimate character repetitions produce high similarity scores that confound duplicate detection.

Woodruff et al. [9] developed the work most closely related to ours: a fully automated pipeline for extracting and analyzing signatures from corporate filings in the context of anti-money laundering investigations.
Their system uses connected component analysis for signature detection, GANs for noise removal, and Siamese networks for author clustering.
While their pipeline shares our goal of large-scale automated signature analysis on real regulatory documents, their objective---grouping signatures by authorship---differs fundamentally from ours, which is detecting digital replication within a single author's signatures across documents.

In the domain of image copy detection, Pizzi et al. [13] proposed SSCD, a self-supervised descriptor using ResNet-50 with contrastive learning for large-scale copy detection on natural images.
Their work demonstrates that pre-trained CNN features with cosine similarity provide a strong baseline for identifying near-duplicate images, a finding that supports our feature extraction approach.

## C. Perceptual Hashing

Perceptual hashing algorithms generate compact fingerprints that are robust to minor image transformations while remaining sensitive to substantive content changes [19].
Unlike cryptographic hashes, which change entirely with any pixel modification, perceptual hashes produce similar outputs for visually similar inputs, making them suitable for near-duplicate detection in scanned documents where minor variations arise from the scanning process.

Jakhar and Borah [12] demonstrated that combining perceptual hashing with deep learning features significantly outperforms either approach alone for near-duplicate image detection, achieving AUROC of 0.99 on standard benchmarks.
Their two-stage architecture---pHash for fast structural comparison followed by deep features for semantic verification---provides methodological precedent for our dual-method approach, though applied to natural images rather than document signatures.

Our work differs from prior perceptual hashing studies in its application context and in the specific challenge it addresses: distinguishing legitimate high visual consistency (a careful signer producing similar-looking signatures) from digital duplication (identical pixel content arising from copy-paste operations) in scanned financial documents.

## D. Deep Feature Extraction for Signature Analysis

Several studies have explored pre-trained CNN features for signature comparison without metric learning or Siamese architectures.
Engin et al. [14] used ResNet-50 features with cosine similarity for offline signature verification on real-world scanned documents, incorporating CycleGAN-based stamp removal as preprocessing---a pipeline design closely paralleling our approach.
Tsourounis et al. [15] demonstrated successful transfer from handwritten text recognition to signature verification, showing that CNN features trained on related but distinct handwriting tasks generalize effectively to signature comparison.
Chamakh and Bounouh [22] confirmed that a simple ResNet backbone with cosine similarity achieves competitive verification accuracy across multilingual signature datasets without fine-tuning, supporting the viability of our off-the-shelf feature extraction approach.

Babenko et al. [23] established that CNN-extracted neural codes with cosine similarity provide an effective framework for image retrieval and matching, a finding that underpins our feature comparison approach.
These findings collectively suggest that pre-trained CNN features, when L2-normalized and compared via cosine similarity, provide a robust and computationally efficient representation for signature comparison---particularly suitable for large-scale applications where the computational overhead of Siamese training or metric learning is impractical.

<!--
REFERENCES for Related Work (see paper_a_references.md for full list):
[3] Bromley et al. 1993 — Siamese TDNN (NeurIPS)
[4] Dey et al. 2017 — SigNet (arXiv:1707.02131)
[5] Hadjadj et al. 2020 — Single sample SV (Applied Sciences)
[6] Li et al. 2024 — TransOSV (Pattern Recognition)
[7] Tehsin et al. 2024 — Triplet Siamese (Mathematics)
[8] Brimoh & Olisah 2024 — Consensus threshold (arXiv:2401.03085)
[9] Woodruff et al. 2021 — AML signature pipeline (arXiv:2107.14091)
[10] Copy-move forgery detection survey — MTAP 2024
[11] Abramova & Böhme 2016 — CMFD in scanned docs (Electronic Imaging)
[12] Jakhar & Borah 2025 — pHash + DL (Info. Processing & Management)
[13] Pizzi et al. 2022 — SSCD (CVPR)
[14] Perceptual hashing survey — ACM TOMM 2025
[15] Engin et al. 2020 — ResNet + cosine on real docs (CVPRW)
[16] Tsourounis et al. 2022 — Transfer from text to signatures (Expert Systems with Applications)
[17] Chamakh & Bounouh 2025 — ResNet18 unified SV (Procedia Computer Science)
[24] Hafemann et al. 2017 — CNN features for signature verification (Pattern Recognition)
[25] Hafemann et al. 2019 — Meta-learning for signature verification (IEEE TIFS)
[26] Zois et al. 2024 — SPD manifold signature verification (IEEE TIFS)
[27] Farid 2009 — Image forgery detection survey (IEEE SPM)
[28] Mehrjardi et al. 2023 — DL-based image forgery detection survey (Pattern Recognition)
[29] Babenko et al. 2014 — Neural codes for image retrieval (ECCV)
-->
