akahana/deepfake-detection-dataset-v3

Deepfake Detection Dataset V3

This dataset contains images and detailed explanations for training and evaluating deepfake detection models. It includes original images, manipulated images, confidence scores, and comprehensive technical and non-technical explanations.

Dataset Structure

The dataset consists of:

• —Original images (image)
• —CAM visualization images (cam_image)
• —CAM overlay images (cam_overlay)
• —Comparison images (comparison_image)
• —Labels (label): Binary classification (real/fake)
• —Confidence scores (confidence_score)
• —Image captions (original_caption, cam_caption)
• —Queries and explanations (query, technical_explanation, non_technical_explanation)

Dataset Statistics

• —Total samples: 179 entries
• —Train split: 161 samples
• —Test split: 18 samples
• —Image format: PNG
• —Labels: Binary (0 for fake, 1 for real)

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("saakshigupta/deepfake-detection-dataset-v3")

# Access the splits
train_data = dataset["train"]
test_data = dataset["test"]

# Example: Get the first sample
sample = train_data[0]
image = sample["image"]
label = sample["label"]
technical_explanation = sample["technical_explanation"]

This dataset can be used to:

1. 1.Train deepfake detection models
2. 2.Evaluate detection accuracy
3. 3.Study manipulation patterns
4. 4.Understand detection interpretability
5. 5.Research technical and non-technical aspects of deepfake detection
6. 6.Develop explainable AI systems for image forensics

Citation

If you use this dataset, please cite:

@misc{gupta2024deepfake,
  title={Deepfake Detection Dataset V3},
  author={Gupta, Saakshi},
  year={2024}
}