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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65395
Volume:
10 |
6 |
Jun 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Engineering
RMD Sinhgad School of Engineering, Warje, Pune, Maharashtra, India
Computer Engineering
RMD Sinhgad School of Engineering, Warje, Pune, Maharashtra, India
Computer Engineering
RMD Sinhgad School of Engineering, Warje, Pune, Maharashtra, India
Computer Engineering
RMD Sinhgad School of Engineering, Warje, Pune, Maharashtra, India
Digital image manipulation has become increasingly common due to the widespread availability of image editing software and artificial intelligence tools. The ability to alter images without leaving obvious visual traces poses significant challenges in fields such as journalism, digital forensics, law enforcement, and social media verification. This paper presents DETECTOO, a web-based image forgery detection system designed to identify potentially manipulated regions in digital images using multiple forensic analysis techniques. The proposed system employs Error Level Analysis (ELA), Edge Detection, and Noise Analysis to detect inconsistencies that may indicate image tampering. The system genertes heatmaps, overlay visualizations, confidence scores, tamper percentage metrics, and downloadable PDF reports to assist users in forensic examination. The frontend is developed using React.js, while FastAPI, OpenCV, and Python are used for backend processing and image analysis. Experimental results demonstrate that the system effectively highlights suspicious regions and provides users with intuitive visual evidence of possible manipulation. The proposed approach offers an accessible, user-friendly, and efficient solution for image forgery detection in real-world applications.
KEYWORDS
In this paper, DETECTOO, a web-based image forgery detection system, was successfully developed and implemented using Error Level Analysis (ELA), Edge Detection, and Noise Analysis techniques. The system effectively identifies suspicious regions in digital images and provides visual evidence through heatmaps and overlay visualizations. In addition, forensic metrics such as confidence score, tamper percentage, ELA score, edge score, and noise score are generated to support the analysis process. The developed system offers an intuitive user interface and automated PDF report generation, making image forgery detection accessible to both technical and non-technical users. The experimental results demonstrate that the proposed system provides an efficient and reliable solution for digital image authenticity verification.
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Publication Date
6/24/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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