# ondyari/FaceForensics

Github of the FaceForensics dataset

Repository: https://github.com/ondyari/FaceForensics
Canonical: https://ross.abutalabs.com/products/faceforensics
Homepage: http://niessnerlab.org/projects/roessler2018faceforensics.html
Language: Python
License: NOASSERTION
License Family: other
Last push: 2022-12-08T01:43:11+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3064, "days_push": 1365, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2764, forks 590 (observed 2026-08-28T04:07:18.586862+00:00)

## What it is
FaceForensics++ is a forensics benchmark dataset of 1000 original YouTube video sequences manipulated with four automated face manipulation methods (Deepfakes, Face2Face, FaceSwap, NeuralTextures), plus the Google/Jigsaw Deep Fake Detection dataset. It includes binary masks and 1000 Deepfakes generation models for training image/video forgery detection and segmentation models.

## Use cases
- train a deepfake detection model
- benchmark face forgery detection methods
- get labeled real and manipulated face videos for research
- train a segmentation model to localize manipulated face regions
- generate augmented deepfake training data with provided models
- study face reenactment and face swapping techniques

## When to choose
- you need a widely used academic benchmark for facial forgery detection
- you want real and fake video pairs with pixel-level masks
- you need diverse manipulation methods (deepfakes, face2face, faceswap, neuraltextures) in one dataset

## When to avoid
- you need data without agreeing to the dataset's terms of use and access request process
- you need high-resolution or unconstrained faces with occlusions
- you need a commercially licensed dataset for product use

## Facets
- artifact type: dataset
- maturity: stable
- function: machine-learning, computer-vision, image-processing, video-processing, data-science
- domain: computer-vision, deep-learning, artificial-intelligence, security
- platform: python, cross-platform
- tags: deepfake-detection, face-forensics, video-dataset, face-manipulation, benchmark, face-swapping, segmentation-masks

## Member repositories
- ondyari/FaceForensics (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:18.586862+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:16:47.248061+00:00, confidence not recorded.
  - readme: https://github.com/ondyari/FaceForensics (fetched 2026-08-28T04:07:18.586862+00:00, sha 03cb448f150d)
  - homepage: http://niessnerlab.org/projects/roessler2018faceforensics.html (fetched 2026-08-29T09:56:27.614410+00:00, sha 860a90c37c9b)
- Data as of 2026-08-30T08:39:29.467469+00:00.