zeeshanaliicreativez/AI-vs-Deepfake-vs-Real
AI vs Deepfake vs Real AI vs Deepfake vs Real is a dataset designed for image classification, distinguishing between artificial, deepfake, and real images. This dataset includes a diverse collection of high-quality 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 AI-generated and deepfake detection models. Label Mappings Mapping of IDs to… See the full description on the dataset page: https://huggingface.co/datasets/zeeshanaliicreativez/AI-vs-Deepfake-vs-Real.
AI vs Deepfake vs Real
AI vs Deepfake vs Real is a dataset designed for image classification, distinguishing between artificial, deepfake, and real images. This dataset includes a diverse collection of high-quality 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 AI-generated and deepfake detection models.
Label Mappings
- Mapping of IDs to Labels:
{0: 'Artificial', 1: 'Deepfake', 2: 'Real'} - Mapping of Labels to IDs:
{'Artificial': 0, 'Deepfake': 1, 'Real': 2}
This dataset serves as a valuable resource for training, evaluating, and benchmarking AI models in the field of deepfake and AI-generated image detection.
Dataset Composition
The AI vs Deepfake vs Real dataset is composed of modular subsets derived from the following datasets:
The dataset is evenly distributed across three categories:
- Artificial (33.3%)
- Deepfake (33.3%)
- Real (33.3%)
With a total of 9,999 entries, this balanced distribution ensures better generalization and improved robustness in distinguishing between AI-generated, deepfake, and real images.