[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122294-en":3,"doc-seo-122294-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122294,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",6,"Technology","Fake Currency Detection using Machine Learning and Computer Vision","Counterfeit currency undermines economic stability, public trust, and business profitability worldwide, while existing checks such as manual inspection and UV scanning remain slow, labor-intensive, and error-prone. The paper presents an automated counterfeit currency detection system built on machine learning and computer vision. It applies ORB and SSIM for feature extraction and matching, and uses specialized algorithms to verify security elements including bleed lines and number panels. Implemented with Python, OpenCV, and TensorFlow, it achieves up to 83% detection for counterfeit notes and about 80% for authentic notes, processing each note in roughly 5 seconds, supporting banking, retail, and law enforcement.","International Journal of Innovative Research in Science  \nEngineering and Technology (IJIRSET)  \n(A Monthly, Peer Reviewed, Refereed, Scholarly Indexed, Open Access Journal)  \nImpact Factor: 8.699 Volume 14, Issue 5 , May 2025  \n|  |\n| --- |\n|  |\n| |[www.ijirset.com](www.ijirset.com |A Monthly)[ |A Monthly](www.ijirset.com |A Monthly), Peer Reviewed & Refereed Journal| e-ISSN: 2319-8753| p-ISSN: 2347-6710|\u003Cbr>Volume 14, Issue 5, May 2025\u003Cbr>|DOI: 10.15680/IJIRSET.2025.1405044|\u003Cbr>Fake Currency Detection using Machine Learning and Computer Vision\u003Cbr>Pavithra1, Amreen Taj S2, Ananya D Deshpande3, Ashutosh Kumar4, Disha K5\u003Cbr>Assistant Professor, Department of Information Science and Engineering, SJBIT, Bangalore, Karnataka, India 1\u003Cbr>U.G. Student, Department of Information Science and Engineering, SJBIT, Bangalore, Karnataka, India 2,3,4,5\u003Cbr>ABSTRACT: TheCirculation of counterfeit money is a critical economic and social problem worldwide, calling for efficient detection systems. Conventional manual processes and UV scanning are time-consuming and prone to errors. This paper suggests an automated counterfeit currency detector system based on machine learning and computer vision. The system utilizes high-level image processing algorithms such as ORB (Oriented FAST and Rotated BRIEF) and SSIM (Structural Similarity Index Measure) for feature extraction and matching, and proprietary algorithms for unique security features such as bleed lines and number panels. Dependent on Python, OpenCV, and TensorFlow implementation, the system has high accuracy in detection of up to 83% against counterfeit notes and 80% against authentic notes and processes relatively fast with the processing time taken being around 5 seconds for each note. The framework poses promising applications for banking, retail, and police forces, toward financial security as well as consumer confidence.\u003Cbr>KEYWORDS: Fake currency detection, Machine learning, Computer vision, ORB, SSIM, Image processing, Python, OpenCV.\u003Cbr>I. INTRODUCTION\u003Cbr>Counterfeit currency continues to be a critical concern for economies and financial institutions across the globe. The widespread circulation of fake currency not only undermines the integrity of monetary systems but also results in severe economic repercussions, including inflation, loss of public trust, and financial setbacks for businesses and individuals alike. Traditional counterfeit detection methods—such as manual inspection, UV scanning, and watermark checking—are not only time-consuming and labor-intensive but also susceptible to human error and inconsistency. These limitations highlight the urgent need for a more advanced, automated, and scalable solution.\u003Cbr>This project aims to address these challenges by developing a sophisticated Fake Currency Detection System leveraging the power of image processing, computer vision, and machine learning algorithms. The proposed system is designed to efficiently analyze and verify the authenticity of currency notes through detailed examination of security features, pattern recognition, and structural similarity measures. By automating the detection process, the system significantly enhances accuracy, reduces the risk of human error, and increases operational efficiency.\u003Cbr>Moreover, the integration of real-time processing capabilities and a user-friendly graphical interface ensures that this solution can be effectively utilized in various domains such as banking, retail, transportation, and law enforcement. Ultimately, this project aspires to strengthen financial security, prevent economic fraud, and pave the way for future innovations in currency authentication technology.\u003Cbr>II. PROBLEMSOFCOUNTERFEITCURRENCY\u003Cbr>The circulation of counterfeit currency constitutes a significant threat to the financial ecosystem, affecting economic integrity, institutional credibility, and public trust. Illegitimate notes infiltrate the money supply, contributing to inflation and reducing the real valu","cbCaiojaigA5UjoJ","https://ap.wps.com/l/cbCaiojaigA5UjoJ","pdf",1251534,1,7,"English","en",105,"# Abstract\n# Introduction\n# Problems of Counterfeit Currency\n# Methodology","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses the critical issue of counterfeit currency circulation and the limitations of manual and UV-based detection methods.\"},{\"question\":\"How does the proposed system detect fake notes?\",\"answer\":\"It uses machine learning and computer vision with ORB and SSIM for feature extraction and matching, along with algorithms to check security features such as bleed lines and number panels.\"},{\"question\":\"What tools and performance are reported in the methodology?\",\"answer\":\"The system is built in Python using OpenCV, scikit-image, and Tkinter, and uses TensorFlow implementation; it reports about 83% accuracy for counterfeit notes and around 80% for authentic notes with ~5 seconds per note.\"}]","Fake Currency Detection using Machine Learning and Computer Vision | PDF",1785809865,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"fake-currency-detection-using-machine-learning-and-computer-vision","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/fake-currency-detection-using-machine-learning-and-computer-vision/122294/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address?","Question",{"text":75,"@type":76},"The paper addresses the critical issue of counterfeit currency circulation and the limitations of manual and UV-based detection methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed system detect fake notes?",{"text":80,"@type":76},"It uses machine learning and computer vision with ORB and SSIM for feature extraction and matching, along with algorithms to check security features such as bleed lines and number panels.",{"name":82,"@type":73,"acceptedAnswer":83},"What tools and performance are reported in the methodology?",{"text":84,"@type":76},"The system is built in Python using OpenCV, scikit-image, and Tkinter, and uses TensorFlow implementation; it reports about 83% accuracy for counterfeit notes and around 80% for authentic notes with ~5 seconds per note.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,117,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":114,"show_sort_weight":115,"slug":116},"Healthcare",40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]