Detecting Fake Images with Python: A Simple Approach Using OpenCV and MD5

Year : 2024 | Volume :11 | Issue : 01 | Page : –
By

Nivedha R.

Ranjana S.

  1. Student, Department of Computer Science, Anna Adarsh College for Women, Chennai, India Department of Computer Science, Anna Adarsh College for Women, Chennai Tamil Nadu India
  2. Assistant Professor Department of Computer Science, Anna Adarsh College for Women, Chennai Tamil Nadu India

Abstract

In today’s modern era, ensuring the authenticity of pictures is crucial. This article presents a straightforward method for identifying fake pictures using the widely used OpenCV and MD5 technologies. OpenCV helps us examine pictures for irregularities such as abnormal colors or shapes, while MD5 is a unique digital fingerprint for each image. Our approach combines these tools to create a robust fraud detection system. OpenCV carefully examines various aspects of pictures, flagging potential areas of manipulation. In addition, MD5 generates a unique code or hash that effectively represents the entire image. We tested our method on various datasets containing both genuine and fake pictures. The results demonstrate that our system accurately detects manipulated regions and verifies authenticity by comparing unique MD5 hashes. This research provides a practical solution for ensuring image integrity in law, medicine, and interactive media.

Keywords: The topics covered are digital images, image forgery, authenticity, conventional techniques, OpenCV, detecting fraud, and MD5.

[This article belongs to Journal of Operating Systems Development & Trends(joosdt)]

How to cite this article: Nivedha R., Ranjana S.. Detecting Fake Images with Python: A Simple Approach Using OpenCV and MD5. Journal of Operating Systems Development & Trends. 2024; 11(01):-.
How to cite this URL: Nivedha R., Ranjana S.. Detecting Fake Images with Python: A Simple Approach Using OpenCV and MD5. Journal of Operating Systems Development & Trends. 2024; 11(01):-. Available from: https://journals.stmjournals.com/joosdt/article=2024/view=144876


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Regular Issue Subscription Review Article
Volume 11
Issue 01
Received April 9, 2024
Accepted April 12, 2024
Published May 3, 2024