LipGAN
Advanced speech-to-lip synchronization for high-fidelity face-to-face translation.
The industry-standard 124,000+ video dataset for training state-of-the-art synthetic media detection models.
The DeepFake Detection Challenge (DFDC) Train Set V2 is a massive, multi-modal dataset developed by Meta AI (formerly Facebook) in collaboration with AWS, Microsoft, and academic partners. Engineered to combat the proliferation of highly realistic synthetic media, the dataset consists of over 124,000 videos across a diverse range of 3,450 actors. Architecturally, the dataset is designed to provide high-fidelity 'ground truth' for both real and manipulated content, utilizing various generative adversarial network (GAN) techniques and face-swapping algorithms. As of 2026, it remains the baseline for benchmarking deepfake detection robustness due to its inclusion of complex lighting conditions, varied backgrounds, and diverse human subjects, which significantly reduces demographic bias in resulting models. The technical architecture of the dataset allows for the training of advanced Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) capable of detecting temporal inconsistencies and frequency-domain artifacts that characterize 2026-era synthetic media. It is distributed under the DFDC agreement, facilitating non-commercial research and development for the global cybersecurity community.
Includes various deepfake methods such as MM/MG (Morphable Models/Generative), DFM (DeepFaceLab), and proprietary GAN-based swaps.
Advanced speech-to-lip synchronization for high-fidelity face-to-face translation.
The semantic glue between product attributes and consumer search intent for enterprise retail.
The industry-standard multimodal transformer for layout-aware document intelligence and automated information extraction.
Photorealistic 4k upscaling via iterative latent space reconstruction.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Data contains balanced representation across age, gender, and skin tones using 3,450 unique human subjects.
Videos are provided at various bitrates and resolutions to simulate real-world social media degradation.
Metadata specifies frame-level manipulations in specific subsets.
A subset of the data features high-resolution source material with consistent studio lighting.
Certain videos include manipulated audio paired with original video to test cross-modal detection.
Hosted on Kaggle's infrastructure for easy cloud-computing integration.
Detecting foreign influence operations using synthetic spokesperson videos.
Registry Updated:2/7/2026
Preventing 'presentation attacks' during remote KYC processes.
Auto-flagging non-consensual synthetic imagery.