Deepfake Vs Real V2

**Deepfake-vs-Real-v2**

Deepfake-vs-Real-v2 is a dataset designed for image classification, distinguishing between deepfake and real images. This dataset includes a diverse collection of high-quality deepfake images to enhance classification accuracy and improve the model’s overall efficiency. By providing a well-balanced dataset, it aims to support the development of more robust deepfake detection models.

**Label Mappings**

• Mapping of IDs to Labels: {0: 'Deepfake', 1: 'Real'}
• Mapping of Labels to IDs: {'Deepfake': 0, 'Real': 1}

This dataset serves as a valuable resource for training, evaluating, and benchmarking AI models in the field of deepfake detection.

**Dataset Composition**

The Deepfake-vs-Real-v2 dataset is composed of modular subsets derived from the following datasets:

• Deepfakes-QA-Patch1
• Deepfakes-QA-Patch2

These subsets contribute to a diverse and high-quality dataset, enhancing the classification performance of deepfake detection models. By integrating multiple sources, this dataset ensures better generalization and improved robustness in distinguishing between deepfake and real images.