Image Forgery Detection Based on Fusion of Lightweight Deep Learning Models

Abstract

The increasing accessibility of digital image editing tools has made image manipulation easier than ever before, creating significant challenges in verifying the authenticity of digital visual content. Copy-move forgery is one of the most widely used image manipulation techniques because duplicated regions originate from the same image, making manual detection difficult. Existing forgery detection approaches often rely on handcrafted feature extraction or a single deep learning architecture, which may exhibit reduced performance when images undergo transformations such as rotation, scaling, compression, illumination variation, and noise addition. This paper presents a fusion-based lightweight deep learning framework for copy-move image forgery detection and localization. The proposed system integrates three efficient convolutional neural network architectures, namely SqueezeNet, MobileNetV2, and ShuffleNet, to extract complementary deep image features. Instead of depending on a single classifier, the extracted features are fused and classified using a Support Vector Machine (SVM), resulting in improved prediction reliability and reduced false detection. To provide visual evidence of manipulation, the framework incorporates a Discrete Cosine Transform (DCT)-based localization module that identifies duplicated image regions and highlights forged areas. The proposed model was implemented using Python with TensorFlow, OpenCV, Scikit-learn, NumPy, and Tkinter. Experimental evaluation was conducted on the MICC-F220 benchmark dataset. The fusion strategy achieved an overall detection accuracy of 90.91%, outperforming individual lightweight convolutional neural network models while maintaining low computational complexity. The proposed framework offers an efficient and practical solution for digital image authentication and is applicable to digital forensics, journalism, law enforcement, cyber security, and multimedia content verification.