ARPAN2026/deepfake-detection-dataset-v3

--- language: en license: mit task_categories: - image-classification pretty_name: Deepfake Detection Dataset V3 size_categories: - 1K<n<10K tags: - computer-vision - deepfake-detection - image-forensics configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: cam_image dtype: image - name: cam_overlay dtype: image - name: comparison_image dtype: image - name: label dtype: class_label: names: '0': fake '1': real - name: confidence_score dtype: float32 - name: original_caption dtype: string - name: cam_caption dtype: string - name: query dtype: string - name: technical_explanation dtype: string - name: non_technical_explanation dtype: string splits: - name: train num_bytes: 279329873.0531309 num_examples: 474 - name: test num_bytes: 31333055.946869068 num_examples: 53 download_size: 32345973 dataset_size: 310662929.0 --- # 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 ```python 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. Train deepfake detection models 2. Evaluate detection accuracy 3. Study manipulation patterns 4. Understand detection interpretability 5. Research technical and non-technical aspects of deepfake detection 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} } ```