Advanced framework for multilevel detection of digital video forgeries.

The rapid expansion of digital media has sparked significant concerns regarding the swift dissemination and potential misuse of forged video content. Existing forgery detection technologies primarily focus on simple forgeries and are still evolving, resulting in a critical gap in the detection of multilevel forgeries, where one forgery is layered over another. This paper presents an innovative framework designed to address this challenge by extracting intricate features from forged frames using attention-augmented convolutional neural networks (AACNNs). A U-Net-based CycleGAN is employed to accurately localize forged regions, enabling a comprehensive analysis that identifies both two- and three-level forgeries by leveraging AACNN's local and global attention mechanisms. To enhance robustness and accuracy, we integrate a model-agnostic meta-learning approach. Our meticulously curated custom dataset, which represents complex forgery scenarios, underpins the effectiveness of our framework. In a 10-shot scenario, the AACNN backbone achieved an impressive accuracy of 98.2%, alongside a sensitivity of 96.3%, specificity of 97.6%, and an F1-score of 96.8%. These results represent a significant advancement in the accuracy and reliability of sophisticated video forgery detection.