A Forensic Approach to Image Clone Detection Using Structural Similarity Index (SSIM) and Error Level Analysis (ELA)
DOI:
https://doi.org/10.69968/ijisem.2026v5i372-83Keywords:
Clone Detection, Image-to-Image Analysis, SSIM Difference Image, Structural Similarity Index Measure (SSIM), Error Level Analysis (ELA)Abstract
This paper proposes a new technique for clone recognition via the combination of the SSIM measure and ELA for detection and assessment of modifications within images.
The SSIM algorithm compares an image and the copy version of the image to determine whether modifications have occurred and find out the extent. By checking out the luminance, contrast, and other structural features of the picture, SSIM computes a similarity score that represents the modification done on the original image. Subsequently, modified areas of the image are highlighted using difference SSIM so that one can see which part of the image differs greatly from the rest in terms of cloning.
Error Level Analysis (ELA) works well with SSIM. ELA identifies deliberately modified sections by recalculating the error levels of two images that were previously saved at different compression levels. Altered regions can be detected because they show disproportionate error levels. The images produced by ELA show an overemphasis of the suspicious regions indicating heatmap spatial analysis to ease the interpretation of results.
This contribution is added to the advancement in image-to-image analysis by giving a systematic means of handling the issues of anomaly detections.
References
[1] Geek for Geeks – Technical resources and tutorials related to image processing, Structural Similarity Index (SSIM), and Error Level Analysis (ELA). Available at: https://www.geeksforgeeks.org
[2] ResearchGate – Scientific papers and discussions on image forensics, SSIM, and ELA techniques for detecting image manipulations. Available at: https://www.researchgate.net
[3] GitHub – Open-source repositories for image processing, SSIM-based comparison tools, and implementations of ELA in Python. Available at: https://github.com
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Copyright (c) 2026 Pinky Kumari, Cheekiri Venkata Murali Krishna, Balijabudda Charan
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