FOCAL VISION: Intelligent AI-Powered Image Forensic and Manipulation Detection System

In this work, we proposed an intelligent AI-powered image forensics and manipulation detection system named FOCAL VISION for detecting, classifying, and localizing manipulated and AI-generated images using advanced deep learning techniques. The proposed framework integrates Convolutional Neural Networks (CNNs), Vision Transformers (ViT), RetinaNet, YOLOv8, EfficientNet, GAN detectors, and Explainable AI methods to perform accurate and reliable forensic analysis of digital images. The system also incorporates preprocessing techniques such as Error Level Analysis (ELA), metadata extraction, image enhancement, and data augmentation to improve detection robustness and classification performance. Unlike traditional image forensic approaches that rely heavily on handcrafted features and manual analysis, the proposed deep learning-based framework automatically learns complex spatial, contextual, and texture-based features directly from digital images. The integration of transfer learning further improves model generalization capability while reducing computational complexity and training time, making the framework suitable for practical real-world deployment. Experimental evaluation demonstrated that the proposed framework achieved high accuracy, precision, recall, and F1-score across multiple image forgery and deepfake detection datasets. The use of ensemble architectures combining CNNs, transformer-based models, object detection frameworks, and GAN detectors significantly improved manipulation detection and localization performance compared to conventional methods. The integration of Explainable AI using Grad-CAM visualization techniques further enhanced the transparency and interpretability of the system by highlighting manipulated regions responsible for model predictions. This improves user trust and assists forensic investigators in understanding and validating automated forensic decisions effectively. Therefore, the proposed FOCAL VISION framework provides an efficient, scalable, and reliable solution for modern digital image forensic analysis. The system can assist cybersecurity professionals, forensic investigators, journalists, and law enforcement agencies in detecting manipulated digital content, reducing manual verification effort, and improving the authenticity verification process of digital media. With large-scale deployment and future enhancements, the framework has the potential to play a significant role in combating digital misinformation, deepfake attacks, and AI-generated media manipulation.

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