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Paper ID:IJSREM61847

TrueSight: A Multi-Modal Framework for AI Image Authenticity Detection

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM61847

Volume:

10 |

4 |

April 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

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.

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Authors

DG

Dhanush G

Department of Information Technology

Sri Shakthi Institute Of Engineering And Technology, Coimbatore, India

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BVK

Boopathy Velan K

Department of Information Technology

Sri Shakthi Institute Of Engineering And Technology, Coimbatore, India

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SK

Sugashini K

Department of Information Technology

Sri Shakthi Institute Of Engineering And Technology, Coimbatore, India

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HB

Hariharan BS

Department of Information Technology

Sri Shakthi Institute Of Engineering And Technology, Coimbatore, India

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AK

Abishek K

Department of Information Technology

Sri Shakthi Institute Of Engineering And Technology, Coimbatore, India

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Abstract

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

Key Words: AI Image Detection Multi-Modal Learning Feature Fusion SPOT-IT Object Detection Deep Learning

Cite This Article

Dhanush G, Boopathy Velan K, Sugashini K, Hariharan BS, Abishek K, "TrueSight: A Multi-Modal Framework for AI Image Authenticity Detection," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-5, April 2026.

Full Article/Conclusion

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

References

[1] A. Radford et al., “Learning Transferable Visual Models From Natural Language Supervision (CLIP),” 2021.  https://arxiv.org/abs/2103.00020    [2] M. Tan and Q. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” 2019.  https://arxiv.org/abs/1905.11946    [3] I. Goodfellow et al., “Generative Adversarial Networks,” 2014.  https://arxiv.org/abs/1406.2661    [4] J. Ho et al., “Denoising Diffusion Probabilistic Models,” 2020.  https://arxiv.org/abs/2006.11239    [5] A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Vision Transformer,” 2020.  https://arxiv.org/abs/2010.11929    [6] D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints (SIFT),” 2004.  https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf    [7] P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” 2001.  https://ieeexplore.ieee.org/document/990517    [8] F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” 2017.  https://arxiv.org/abs/1610.02357    [9] Bayar and Stamm, “A Deep Learning Approach to Universal Image Manipulation Detection,” 2016.  https://arxiv.org/abs/1609.07093    [10] Cozzolino et al., “Noiseprint: CNN-based Camera Model Fingerprint,” 2018.  https://arxiv.org/abs/1811.07853 

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Publication Date

5/1/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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