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Python Image Forgery Detection using MD5 OpenCV

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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About this project

Python Image Forgery Detection using MD5 OpenCV

Objective: Detect image forgery by verifying image integrity using cryptographic hashing and image processing.

Context: With the rise of digital image manipulation in India, verifying the authenticity of images is crucial for journalism, law enforcement, and digital evidence.

What you'll build: You will develop a Python-based system that uses OpenCV for image handling and hashlib to generate and compare MD5 hashes of images. The project will implement logic to detect discrepancies indicating forgery and visualize results, with an optional user interface for ease of use.

Deliverables: A working prototype with code, sample test images, and a brief report demonstrating forgery detection results.

Milestones

1. Image input and hash generation

10 marks 7d

Collect at least five sample images (original and suspected forgeries). Write a Python script that loads an image using OpenCV and computes its MD5 hash. Submit your script and a CSV file listing each image filename with its corresponding MD5 hash. Ensure your code runs without errors and hashes match expected outputs for identical images.

2. Comparison logic implementation

10 marks 7d

Implement Python logic to compare the MD5 hashes of two images and determine if they are identical or potentially forged. Submit your comparison function and a test script that checks at least three image pairs, clearly printing 'Match' or 'Forgery Suspected' for each. Ensure results are accurate and code is well-commented.

3. Forgery detection visualization

15 marks 7d

Create a visualization that displays both images side by side, highlighting differences if a forgery is suspected. Use OpenCV to annotate or mark mismatched regions, if possible. Submit your visualization code and sample output images; ensure the visualizations clearly communicate detection results to the user.

4. UI integration (optional)

15 marks 9d

Integrate your forgery detection pipeline into a simple UI (CLI or GUI). Allow users to select or input image files, run detection, and view results and visualizations. Submit your UI code and a short demo video or screenshots showing the full workflow. Ensure the UI is functional and user-friendly.

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Upcoming sessions

Session	Window	Enrolled
Python Image Forgery Detection using MD5 OpenCV	11 Jun 2026 to 10 Jun 2028	0

Skills you'll learn

Cryptography (MD5)Image ProcessingPython Programming

Tools used

Python OpenCV hashlib

Prerequisites

MD5 hashing OpenCV basics

Available mentors

Priyang Kumar

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Python Image Forgery Detection using MD5 OpenCV

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