Multi-Modal Deep Learning for Image Forgery Detection: A Synergistic Fusion Approach Combining Visual Artifacts and Metadata Consistency Analysis

Abstract

Multimedia data have been continuously increasing in magnitude, and so has the sophistication of manipulation methods, thereby making the digital forensic investigation process more complicated. The easy access to sophisticated image editing software and AI-generated materials has brought up the issue of information integrity, the reliability of legal evidence, and public trust. Traditional image forensics methods are usually concerned with either the detection of visual artifacts based on convolutional neural networks (CNNs) or based on metadata analysis, frequently independently of each other. This paper presents a multi-modal fusion paradigm, comprising visual feature-based feature extraction and metadata inconsistency-based detectors, to improve the classification strength. A two-stream design is used, comprising a high-level visual artifact capturing the transfer learning-based MobileNetV2 network and an XGBoost classifier that analyses EXIF metadata discrepancies. The heterogeneous representations are merged in a feature-level fusion strategy to generate a final authenticity prediction. It was tested on individual datasets and a compiled dataset of 26,023 images from CoMoFoD, CG-1050 and CASIA v1 and v2. The suggested approach had an overall accuracy of 83.85%, which was higher than the visual-only (68.61%) and metadata-only (75.85%) baselines. These findings show that complementary visual and metadata cues are much more useful in detection, while the use of a lightweight backbone enables efficient, high-throughput forensic analysis suitable for real-world deployment.