Deepfake Vs Real: Images for Classification Model Training

Deepfake vs Real is a dataset for image classification, distinguishing between deepfake and real images. It includes a diverse collection of high-quality deepfake images and aims to support the development of more robust detection models. The dataset was created by prithivMLmods and was last updated on February 19, 2025.

Use Cases

• Train binary image classifiers based on the 'Deepfake' vs. 'Real' labels mentioned in the description.
• Benchmark model performance on a dataset described as containing diverse, high-quality deepfake images.
• Improve model efficiency for detection tasks using a dataset described as well-balanced.

Strengths

• Dataset is explicitly designed for image classification between 'Deepfake' and 'Real' categories.
• Description mentions a 'diverse collection of high-quality deepfake images' intended to enhance accuracy.
• The dataset aims to be 'well-balanced' to support robust model development.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

huggingface

Freshness

Last updated 2025-02-19 21:13:31; freshness should be verified.