Advanced Methods for Detecting and Pinpointing Forgeries in Digital Visual Content

Abstract

Digital media manipulation has become increasingly sophisticated with the advancement of artificial intelligence and deep learning technologies, necessitating robust forgery detection mechanisms. This empirical study presents an optimized approach for detecting and localizing forgeries in digital images and videos through a comprehensive analysis of multiple detection algorithms and feature extraction techniques. The research employs a hybrid methodology combining convolutional neural networks (CNNs) with traditional forensic analysis methods to achieve enhanced accuracy in identifying manipulated content. Our experimental evaluation was conducted on a dataset comprising 5,000 authentic and 5,000 manipulated images, along with 1,200 video sequences containing various types of forgeries including copy-move, splicing, and deepfake manipulations. The proposed optimization framework achieved an average detection accuracy of 94.7% for images and 91.3% for videos, with localization precision of 89.2% and 85.8% respectively. The study demonstrates significant improvements over existing state-of-the-art methods through feature fusion techniques and adaptive threshold optimization. Results indicate that the integration of spatial and temporal features with attention mechanisms substantially enhances forgery detection capabilities while reducing false positive rates by 23%. The research contributes to the field of digital forensics by providing a scalable solution for real-time forgery detection applications.