Bank Card Tampering Detection Using Computer Vision

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Bank Card Tampering Detection Using Computer Vision

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Overview
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Detail Description

1. Abstract

The rapid growth of digital technology, identity verification has become an important requirement for many organizations. However, cases of forged or tampered identification cards such as bank cards, PAN cards, Aadhaar cards, and other identity documents are increasing. Detecting whether an ID card is genuine or tampered is therefore a critical task for ensuring security and preventing fraud.

This project focuses on detecting tampering in bank cards using computer vision techniques. The system compares the uploaded card image with the original reference image to identify differences. Image processing techniques such as image resizing, grayscale conversion, thresholding, contour detection, and similarity index calculation are used to analyze the images.

The model evaluates the similarity between the original card image and the uploaded image and highlights the differences using bounding rectangles around the detected tampered regions. The system then provides a similarity score to determine whether the card has been tampered with or not.

This project demonstrates the application of computer vision and image processing techniques in fraud detection and identity verification systems.

2. Objectives

The main objectives of this project are:

To understand the concept of identity card tampering detection using computer vision.
To analyze differences between original and tampered card images.
To apply image preprocessing techniques such as resizing, grayscale conversion, and thresholding.
To compute similarity index between images for comparison.
To detect contours and highlight tampered regions in the image.
To build a system capable of detecting tampered ID cards automatically.
To visualize differences between original and modified card images.

3. Existing System

Currently, most organizations verify identity documents using:

Manual verification by employees
Online database verification using ID numbers
Basic visual inspection of documents

Limitations of Existing Systems

Manual verification is time-consuming.
Human inspection may fail to detect small tampering changes.
Online verification requires internet access and database connectivity.
Difficult to detect visually modified or forged ID cards.
Lack of automated image-based verification systems.

These limitations highlight the need for an automated system capable of detecting document tampering using computer vision.

4. Proposed System

The proposed system uses computer vision techniques to detect tampering in bank cards or identity cards.

In this system:

Users upload an image of the ID card.
The uploaded image is compared with the original reference image.
Image preprocessing techniques such as resizing and grayscale conversion are applied.
Structural Similarity Index (SSIM) is used to measure similarity between images.
Thresholding and contour detection methods identify areas where changes have occurred.
Bounding rectangles highlight the tampered regions.
The similarity score helps determine whether the card is genuine or tampered.

This system provides a fast and automated solution for identity verification.

5. Implementation Procedure

The implementation of this project consists of the following steps:

Step 1: Image Input

The system takes two images as input:

Original card image
User-uploaded card image

Step 2: Image Preprocessing

Resize the uploaded image to match the original image size
Convert images into grayscale format
Normalize image format and dimensions

Step 3: Similarity Detection

Calculate the Structural Similarity Index (SSIM) between the two images
Determine the similarity score

Step 4: Image Thresholding

Apply thresholding to highlight areas of difference between images

Step 5: Contour Detection

Detect contours in the threshold image using image processing libraries

Step 6: Bounding Box Creation

Draw bounding rectangles around detected contours
Highlight the tampered areas on the card

Step 7: Result Visualization

Display the following images:
Original Image
Tampered Image
Difference Image
Threshold Image
Show the similarity score to determine whether the card is tampered or genuine.

6. Software Requirements

The software tools used in this project include:

Python – Programming language
Jupyter Notebook / Google Colab – Development environment
OpenCV – Image processing library
NumPy – Numerical computations
Matplotlib – Image visualization
Scikit-image – Structural similarity calculation
Imutils – Image processing utilities

7. Hardware Requirements

Minimum Hardware Requirements:

Processor: Intel i5 or higher
RAM: 8 GB or higher
Storage: 256 GB or higher
Laptop or Desktop Computer
Internet connection (optional for dataset download)

8. Advantages of the Project

Detects tampered ID cards automatically.
Reduces manual verification effort.
Provides fast and accurate identity verification.
Highlights tampered regions visually using bounding boxes.
Can be used for security and fraud detection systems.
Improves reliability of identity verification processes.
Demonstrates practical use of computer vision in document verification.

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