International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 2
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Image Forgery Detection Using Hybrid Deep

Author(s) Mr. Hari K, Mr. Dhinakaran R, Mr. Hemanth V, Ms. Vijayalakshmi R
Country India
Abstract Digital image forgery has become increasingly sophisticated with the advancement of image editing tools and AI-based generative models. Detecting such manipulations is critical for applications in digital forensics, journalism, and legal investigations. This paper presents DeepScan, a hybrid image forgery detection system that integrates classical forensic techniques with deep learning to improve accuracy, robustness, and explainability. The system employs Error Level Analysis (ELA) and Photo Response Non-Uniformity (PRNU) noise profiling to capture compression and sensor inconsistencies, while a Convolutional Neural Network (CNN) performs multi-class classification of images into Authentic, Forged, and AI-Generated categories. A fusion decision engine combines outputs from both analysis paths to generate a final verdict with confidence. Additionally, Grad-CAM is used to provide visual explanations through heatmaps, and SHA-256 hashing ensures forensic integrity. Experimental results demonstrate that the hybrid approach outperforms standalone methods, achieving improved accuracy and reliability. DeepScan provides a transparent, explainable, and practical solution for modern image forgery detection.
Keywords Image Forgery Detection, CNN, Grad-CAM, ELA, PRNU, Explainable AI, Digital Forensics, Deep Learning
Field Engineering
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-29
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E-ISSN 2582-2160
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