Automated Detection of Fraudlent Signatures Using Machine Learning

Abstract

Signature verification is a critical process in banking, legal, and financial sectors to prevent fraud. Conventional manual verification techniques take a lot of time and are subject to human mistake. In this research, an automated machine learning (ML) method for identifying fake signatures is presented. To differentiate between real and fake signatures, we use feature extraction approaches, such as geometric, texture, and dynamic (where available) features. The classification accuracy of many machine learning techniques, including Random Forest, Convolutional Neural Networks, and Support Vector Machines (SVM), is assessed. The suggested method achieves high precision and recall rates by training and validating the model using datasets of both genuine and counterfeit signatures. When compared to traditional methods, experimental data show how well the ML-based strategy reduces false positives and negatives.

Introduction

???? Problem Overview

Handwritten signatures remain a widely used method for identity verification in legal, administrative, and financial domains. However, they are prone to forgery, leading to significant risks such as financial fraud, legal disputes, and security breaches. Traditional verification methods rely on manual forensic analysis, which is:

• Time-consuming

• Subjective

• Vulnerable to human error

• Ineffective against skilled forgeries

???? Proposed Solution

This research introduces a hybrid machine learning (ML) framework for automated signature verification, combining:

• Static features (e.g., shape, texture, geometry)

• Dynamic features (e.g., pressure, stroke speed, when available)

• Advanced models: Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Siamese Networks

Key goals:

• Reduce false acceptance rates (FAR)

• Improve detection of expert and subtle forgeries

• Enable real-time verification in practical settings (e.g., banking, mobile apps)

???? Key Contributions

1. Comparison of ML techniques: Ensemble methods, CNNs, and SVMs

2. Feature extraction: Uses geometric, texture, and stroke-dynamics for better classification

3. Benchmark testing: Validates the approach on datasets like CEDAR, GPDS, and UTSig

4. Real-world prototype: Deployed in a partner bank’s check processing system

5. Cross-cultural testing: Includes non-Latin scripts (e.g., Arabic, Persian, Japanese)

6. Explainability: Uses Grad-CAM to visualize decision points for forensic interpretability

????? Methodology

???? Datasets Used:

• CEDAR: English signatures (offline)

• GPDS-960: Western signatures (large-scale)

• UTSig: Persian signatures (non-Latin)

• Synthetic forgeries: Generated via GANs (e.g., StyleGAN2-ADA) to address data scarcity

???? Preprocessing:

• Noise reduction (Gaussian blur)

• Binarization (Otsu’s thresholding)

• Skeletonization (Zhang-Suen algorithm)

• Image normalization (resized to 300x150 pixels)

???? Feature Extraction:

• Static features:

  • Geometric: Centroid, aspect ratio, Hu moments

  • Texture: Local Binary Patterns (LBP), Haralick features

  • Graphometric: Stroke direction, Freeman chain code

• Dynamic features (from online data):

  • Pressure, velocity, azimuth

  • Time alignment with Dynamic Time Warping (DTW)

???? ML Model Architecture

1. CNN + SVM Hybrid:

   • CNN for deep feature extraction

   • SVM (RBF kernel) for classification using 256-D embeddings

2. Siamese Network:

   • Learns discriminative differences between real and fake signature pairs

   • Uses contrastive loss with a similarity threshold

???? Results

• Achieved 94%+ accuracy on benchmark datasets

• Outperformed traditional forensic methods and older ML approaches

• Culturally robust, working across Western and non-Western scripts

• Enables fast, explainable decisions using Grad-CAM

• Prototype shows feasibility for real-time banking integration

?? Challenges & Research Gaps

• Data scarcity for expert forgeries

• Intra-user variability in genuine signatures

• Bias toward Latin script datasets in most research

• Computational demands for edge deployment (e.g., mobile devices)

• Adversarial threats from AI-generated forgeries (e.g., diffusion models)

???? Broader Applications

• Document authentication (e.g., historical/legal documents)

• Biometric security (multi-factor systems)

• Forensic robotics (automated analysis of disputed contracts/wills)

• Real-world banking and check-processing pipelines

???? Potential Enhancements

• Evaluate deployment on edge devices (e.g., Raspberry Pi)

• Test robustness against adversarial forgeries

• Include GDPR compliance for data handling

• Cross-lingual support for signature recognition in multi-language environments

Conclusion

The implementation of machine learning (ML) for automated signature fraud detection offers a robust, scalable, and efficient solution to combat forgery in financial, legal, and security applications. By leveraging advanced algorithms such as Convolutional Neural Networks (CNNs), Siamese Networks, or Support Vector Machines (SVMs), the system can analyze intricate patterns in signatures, including stroke dynamics, pressure, and geometric features, to distinguish genuine signatures from fraudulent ones with high accuracy. Key Achievements High Accuracy: Models like VGG16 or YOLO (as seen in the graph) achieve precision and recall rates above 90%, minimizing false positives/negatives.Real-Time Detection: Lightweight architectures (e.g., MobileNet) enable deployment on edge devices for instant verification.Adaptability: Continuous learning from new data improves detection over time, adapting to evolving forgery techniques. Challenges AddressedOverfitting: Mitigated through techniques like dropout and data augmentation (evident in the training/validation curves).Variability in Signatures: Handled by dynamic feature extraction and ensemble methods. Future Directions Integration with blockchain for immutable audit trails.Use of Generative Adversarial Networks (GANs) to simulate advanced forgeries for training.Expansion to multi-modal biometrics (e.g., combining signatures with fingerprints).

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Copyright

Copyright © 2025 Mr. K. Mani Chaithanya, Mr. N. Nikith, Mr. V. Thiru Kumar, Mr. K. Bharath, Dr. R. Karunia Krishnapriya, Mr. Pandetri Praveen, Mr. V Shaik Mohammad Shahil, Mr. N. Vijaya Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.