Unmasking the Masked: A Classical Machine Learning Pipeline for Detecting Forged Receipts

Title

Unmasking the Masked: A Classical Machine Learning Pipeline for Detecting Forged Receipts

Creator

Neerakan, Marita J; Sudhakar, T.; Logeshwaran, J.

Description

The abundance of digital and paper document forgery requires strong automated detection tools against financial fraud. This research provides a classical machine learning method for forged receipt detection using multimodal features from image and text modalities. The approach entailed designing a feature set to obtain textural and statistical attributes from receipt images via Local Binary Patterns (LBP) and Canny edge detection, along with structural features obtained from the associated text files. Another demanding issue in this area is the excessive class imbalance between genuine and forged documents. To overcome this issue, Synthetic Minority Over-sampling Technique (SMOTE) is used to create a balanced training dataset. The models are assessed using the macro F1-score, precision, recall, PR AUC and ROC AUC to address class imbalance. The enhanced detection of the minority class is achieved using SMOTE, while hyperparameter tuning leads to the improvements in performance. The final Tuned Support Vector Machine model achieves a macro F1-score of 0.5429, and it has the highest recall on forged receipts, demonstrating that it detects more histories of tampered documents effectively. This research sets a good baseline for receipt forgery detection and emphasizes that class imbalance solving is a key towards creating a working system. 2025 IEEE.

Source

International Conference on NexGen Networks and Cybernetics, IC2NC 2025 - Proceedings;pp.989-994

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Document Forgery Detection; Local Binary Patterns; Machine Learning; Support Vector Machine (SVM)

Coverage

Neerakan M.J., Christ University, Dept. Computer Science, Karnataka, Bengaluru, India; Sudhakar T., Christ University, Dept. Computer Science, Karnataka, Bengaluru, India; Logeshwaran J., Christ University, Dept. Computer Science, Karnataka, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833159484-8;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/IC2NC67409.2025.11376086

https://www.scopus.com/pages/publications/105035586299?origin=resultslist