BanglaQR-Quish: A Balanced Synthetic QR Image Dataset for Quishing Analysis in Bangla QR Payment Systems

--------------------------------------------------------------------- 1. DATASET DESCRIPTION --------------------------------------------------------------------- BanglaQR-Quish contains 50,000 unique synthetic QR-code images created for research on quishing attacks in Bangla QR payment systems. The dataset is equally divided into 25,000 benign and 25,000 malicious images. The benign QR codes represent synthetic static merchant payment payloads containing merchant information, merchant category, city, country code, currency code, and provider-style account information. The malicious QR codes were generated from benign payloads using two attack scenarios: provider identifier manipulation and payment redirection. The dataset is designed for QR-code classification, quishing detection, computer vision, machine learning, deep learning, and digital payment security research. --------------------------------------------------------------------- 2. CLASS DISTRIBUTION (Total: 50,000 Images) --------------------------------------------------------------------- 1. BENIGN (25,000 images): - Valid synthetic merchant payment QR codes. 2. MALICIOUS (25,000 images): - Structurally valid and scannable modified QR codes. - provider_gui_url_injection: 12,500 images. - payment_redirection: 12,500 images. Five provider-style families are included: bKash, Nagad, TallyPay, Mutual Trust Bank, and DBBL/NexusPay. Each family contains 5,000 benign, 2,500 URL-injection, and 2,500 payment-redirection images. --------------------------------------------------------------------- 3. FILE STRUCTURE & COLUMNS --------------------------------------------------------------------- Folders and Files: A. benign/ Contains 25,000 benign PNG images. B. malicious/ Contains 25,000 malicious PNG images. C. metadata.csv Contains information for all 50,000 images. D. README.txt Describes the dataset, labels, files, and possible uses. metadata.csv Columns: A. image_name: Unique identifier of the QR-code image. B. label: Classification label: benign or malicious. C. attack_type: benign_valid, provider_gui_url_injection, or payment_redirection. D. provider_family: bkash, nagad, tallypay, mtb, or dbbl_nexuspay. E. source_benign_image_name: Identifies the benign source used to generate a malicious image. This field is empty for benign samples.