Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Neerakan, Marita J; Sudhakar, T.; Logeshwaran, J. Title A name given to the resource Unmasking the Masked: A Classical Machine Learning Pipeline for Detecting Forged Receipts Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Conference on NexGen Networks and Cybernetics, IC2NC 2025 - Proceedings;pp.989-994 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC2NC67409.2025.11376086" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC2NC67409.2025.11376086</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035586299?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035586299?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Document Forgery Detection; Local Binary Patterns; Machine Learning; Support Vector Machine (SVM) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159484-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online