Spotting the Differences between Two Images

Abstract

This paper presents a generalized solution to the classical problem of spotting the differences between two images. In this digital era, the authenticity of an image has become a big challenge to the researchers and engineers in the field of computer vision and image processing. Due to the rapid developments in digital technology, creation of photographic fakes and image manipulation has become easily accessible to everyone. With the availability of open-source editing software tools, the possibility of various image manipulations like image forgery, image tampering and image splicing have become almost inevitable. This paper addresses the problem by using classical image processing techniques along with the state-of-the-art YOLOv8 deep learning object detection algorithm. The results obtained are very promising when the model is trained on a synthetic dataset of 700 pairs of images. The uniqueness of the dataset is that each pair of images is different from any other pair of images and the number of differences between any two images may vary from 1 to 50.

Keywords

• ORB
• SIFT
• SSIM
• CNN
• YOLO

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