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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM61847
Volume:
10 |
4 |
April 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.
Department of Information Technology
Sri Shakthi Institute Of Engineering And Technology, Coimbatore, India
Department of Information Technology
Sri Shakthi Institute Of Engineering And Technology, Coimbatore, India
Department of Information Technology
Sri Shakthi Institute Of Engineering And Technology, Coimbatore, India
Department of Information Technology
Sri Shakthi Institute Of Engineering And Technology, Coimbatore, India
Department of Information Technology
Sri Shakthi Institute Of Engineering And Technology, Coimbatore, India
The rapid advancement of generative artificial intelligence has enabled the creation of highly realistic synthetic images, making it increasingly difficult to distinguish them from real-world photographs. This paper presents TrueSight, a multi-modal framework designed for accurate detection of AI-generated images. The system integrates semantic, visual, frequency, and noise-based feature extraction techniques into a unified pipeline. A calibrated XGBoost classifier is used to improve prediction accuracy through feature fusion. In addition, a similar object detection module named SPOT-IT is incorporated to identify repeated or duplicated objects within images using hybrid matching techniques. The system is implemented using a modular Flask-based architecture with independent services and centralized routing. Experimental results demonstrate that the proposed framework achieves high accuracy, robustness, and explainability, making it suitable for digital forensics and image verification applications.
KEYWORDS
This paper presented TrueSight, an integrated AI vision system combining deep learning and hybrid object detection. The system achieves high accuracy and provides explainable results. Future work includes real-time optimization and video analysis
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Publication Date
5/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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