Face Swapping

FaceForensics++ is a forensics dataset consisting of 1000 original video sequences that have been manipulated with four automated face manipulation methods: Deepfakes, Face2Face, FaceSwap and NeuralTextures. The data has been sourced from 977 youtube videos and all videos contain a trackable mostly frontal face without occlusions which enables automated tampering methods to generate realistic forgeries. Source: https://github.com/ondyari/FaceForensics Image Source: https://github.com/ondyari/FaceForensics

Celeb-DF is a large-scale challenging dataset for deepfake forensics. It includes 590 original videos collected from YouTube with subjects of different ages, ethnic groups and genders, and 5639 corresponding DeepFake videos. Source: https://github.com/danmohaha/celeb-deepfakeforensics Image Source: https://github.com/danmohaha/celeb-deepfakeforensics

The DFDC (Deepfake Detection Challenge) is a dataset for deepface detection consisting of more than 100,000 videos. The DFDC dataset consists of two versions: - Preview dataset. with 5k videos. Featuring two facial modification algorithms. - Full dataset, with 124k videos. Featuring eight facial modification algorithms Source: The Deepfake Detection Challenge (DFDC) Preview Dataset

AFLW2000-3D is a dataset of 2000 images that have been annotated with image-level 68-point 3D facial landmarks. This dataset is used for evaluation of 3D facial landmark detection models. The head poses are very diverse and often hard to be detected by a CNN-based face detector. Source: https://www.tensorflow.org/datasets/catalog/aflw2k3d Image Source: http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm

WildDeepfake is a dataset for real-world deepfakes detection which consists of 7,314 face sequences extracted from 707 deepfake videos that are collected completely from the internet. WildDeepfake is a small dataset that can be used, in addition to existing datasets, to develop more effective detectors against real-world deepfakes. Source: https://github.com/deepfakeinthewild/deepfake-in-the-wild Image Source: https://github.com/deepfakeinthewild/deepfake-in-the-wild

DeeperForensics-1.0 represents the largest face forgery detection dataset by far, with 60,000 videos constituted by a total of 17.6 million frames, 10 times larger than existing datasets of the same kind. The full dataset includes 48,475 source videos and 11,000 manipulated videos. The source videos are collected on 100 paid and consented actors from 26 countries, and the manipulated videos are generated by a newly proposed many-to-many end-to-end face swapping method, DF-VAE. 7 types of real-world perturbations at 5 intensity levels are employed to ensure a larger scale and higher diversity. Image Source: https://github.com/EndlessSora/DeeperForensics-1.0

HOD is a dataset for 3D object reconstruction which contains 35 objects, divided into two subsets named Sculptures and Daily Objects. The Sculptures has five human sculptures with complex geometries and pure white textures. The Daily Objects consists of 30 daily objects with various shapes and appearances. All of the Sculptures and nine of the Daily Objects are paired with high-fidelity scanned meshes as ground truth geometries for evaluation. Source: Reconstructing Hand-Held Objects from Monocular Video Image Source: https://arxiv.org/pdf/2211.16835v1.pdf

We created a new dataset, named DFDM, with 6,450 Deepfake videos generated by different Autoencoder models. Specifically, five Autoencoder models with variations in encoder, decoder, intermediate layer, and input resolution, respectively, have been selected to generate Deepfakes based on the same input. We have observed the visible but subtle visual differences among different Deepfakes, demonstrating the evidence of model attribution artifacts.

VideoForensicsHQ is a benchmark dataset for face video forgery detection, providing high quality visual manipulations. It is one of the first face video manipulation benchmark sets that also contains audio and thus complements existing datasets along a new challenging dimension. VideoForensicsHQ shows manipulations at much higher video quality and resolution, and shows manipulations that are provably much harder to detect by humans than videos in other datasets. VideoForensicsHQ contains 1,737 videos of speaking faces (44% male, 56% female), with 8 different emotions, most of them of “HD” resolution. The videos amount to 1,666,816 frames. Source: VideoForensicsHQ: Detecting High-quality Manipulated Face Videos

A high-resolution version of VGGFace2 for academic face editing purposes. This project uses GFPGAN for image restoration and insightface for data preprocessing (crop and align).