Deepfake Image Classification Using Decision (Binary) Tree Deep Learning

Abstract

The unprecedented rise of deepfake technologies, leveraging sophisticated AI like Generative Adversarial Networks (GANs) and diffusion-based models, presents both opportunities and challenges in terms of digital media authenticity. In response, this study introduces a novel deep neural network ensemble that utilizes a tree-based hierarchical architecture integrating a vision transformer, ResNet, EfficientNet, and DenseNet to address the pressing need for effective deepfake detection. Our model exhibits a high degree of adaptability across varied datasets and demonstrates state-of-the-art performance, achieving up to 97.25% accuracy and a weighted F1 score of 97.28%. By combining the strengths of various convolutional networks and the vision transformer, our approach underscores a scalable solution for mitigating the risks associated with manipulated media.

Keywords

• deepfake classification
• decision trees
• vision transformers
• convolutional neural network (CNN)