Deepfake detectionComputer visionVideo

DeepfakeBench

Dozens of open face-swap detectors for video and photo under one codebase with ready weights. A detector errs in both directions: its output is a reason for a human to check, not proof of a forgery.

Developer
The Chinese University of Hong Kong, Shenzhen (SCLBD), China
First release
Jun 2023
Latest release
Aug 2025
Sizes
Xception- and EfficientNet-class detectors, tens of millions of parameters
License
Non-commercial onlyCC BY-NC 4.0 - non-commercial use only
Running
On your own serverAlso runs without a GPU
Industries
Security, Media and production, Finance, Public sector
Deploy on your server GitHub

What it does

  • First-pass check of a submitted video or selfie
  • Comparing several detectors on your own data
  • Fine-tuning a detector for your own flow of applications
  • Researching how robust identity checks are

Where it is used

Information securityMedia and fact-checkingBanks and insurancePublic sector

Hardware requirements

LaptopLaptop or regular PC, up to 8 GB of VRAM — smaller versions
fits
1 GPUOne GPU with 16–80 GB — mid-size versions
fits
ClusterServer with several GPUs — flagship versions
no versions

Versions

  1. Обновление кода и наборов данных
  2. Расширенный набор детекторов и весов
  3. DeepfakeBench (первый выпуск)

How to run it

On your own serverWeights are downloaded from Hugging Face and served with vLLM (load and API) or llama.cpp (modest hardware). The model then runs in a closed network with no per-request fees.
How much hardware you needCalculate the VRAM for the model size, context length and number of concurrent requests.Open the hardware calculator

I can set this up end to end: pick the model size, deploy it on your server and connect it to your systems.

Frequently asked questions

Can DeepfakeBench be used in a commercial project?

No. License: CC BY-NC 4.0 - non-commercial use only. A commercial product needs a different model or a separate agreement with the rights holder.

What hardware does DeepfakeBench need?

At minimum: Laptop or regular PC, up to 8 GB of VRAM — smaller versions. Some versions also run on an ordinary CPU, without a GPU. You can calculate the exact VRAM for your model size and context in the hardware calculator.

Does DeepfakeBench support Russian?

Language does not matter for this model: it does not work with text.

Where can I download DeepfakeBench and what does it cost?

The DeepfakeBench weights are open and free to download. You only pay for the hardware it runs on and for the setup. Source links are at the bottom of this page.

How I deploy it for clients

  1. SelectionI pick the model size for your task and hardware and test it on your examples.
  2. DeploymentI deploy it on your server or in a closed network and provide an API.
  3. Fine-tuningI fine-tune it on your data (LoRA) or connect a knowledge base — whichever is cheaper for the task.
  4. IntegrationI connect it to your CRM, ERP, bot, website or team chat and set up monitoring.

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DetailsDeepfake detectionUniversalFakeDetectUniversity of Wisconsin-Madison · USACommercial use allowed

An early and still used approach: a simple classifier trained on top of a frozen CLIP that transfers to unseen generators. It errs in both directions - the output needs a human check.

DetailsDeepfake detectionTruForGRIP, University Federico II of Naples · ItalyNon-commercial only

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Details

Source: github.com/SCLBD/DeepfakeBench. Data checked against the model card on 22 Sep 2026. Have a lawyer review the license before commercial launch.