Comprehensive Deepfake Detection Dataset: Real and Synthetic Frames from Roop and Akool AI Technologies

This dataset is a cutting-edge resource for deepfake detection, containing 110,694 frames extracted from 480 videos. It features two primary categories: deepfake and real frames. The deepfake frames (106,948) were generated from 450 videos using advanced AI tools such as Roop Faceswapper and Akool AI, while the real frames (3,746) were derived from 30 authentic videos. The dataset is meticulously curated to ensure diversity, balance, and high-quality representation, making it an invaluable resource for training and evaluating deepfake detection models.

The dataset collection process was conducted with ethical approval from Daffodil International University, ensuring adherence to ethical standards for data collection and research in deepfake detection systems.

Key Features:
Total Videos: 480 (450 deepfake, 30 real).
Frame Distribution: 106,948 deepfake frames and 3,746 real frames.
Deepfake Generation Tools: Leveraged state-of-the-art technologies like Roop Face-Swapper and Akool AI for synthetic video creation.
Demographic Diversity: Includes frames from 15 males and 15 females, ensuring varied representation across facial features, lighting conditions, and environments.
Balanced Dataset: Carefully curated for fair model training and evaluation.
Applications: Ideal for developing scalable, real-time detection systems in cybersecurity, digital forensics, and media integrity verification.

This dataset offers an unparalleled opportunity to explore and enhance deepfake detection models, addressing the challenges posed by synthetic media with a diverse and high-quality benchmark.