Deepfake Scanner by Deepware.

Deepware Scanner (CLI)

This repository contains the command-line deepfake scanner tool with the pre-trained models that are currently used at deepware.ai.

To get an in-depth review about the scanner and the training process please refer to deepware.md. Here we will discuss the usage of the command-line scanner.

Installation

Make sure you have a nvidia gpu with cuda support. Scanner can run both on Windows and Linux.

On Linux
  • Clone the repo or download it as a zip and extract to a directory.
  • Install the dependencies listed in requirements file with pip install -r requirements.txt
  • Download the pre-trained model and place it in the weights directory.
On Windows

We packed all the requirements in a portable zip file including the model. You can download the zip file and start scanning right away.

Usage

The scanning script is scan.py and it has four command line arguments. Here's the usage printed by the script.

scan.py

Let's dive into the arguments.

  • scan_dir is the directory of videos, alternatively a file with list of video paths is supported.
  • models_dir is the directory pre-trained models are stored. There can be multiple models and they will be ensembled automatically.
  • cfg_file is the config file. You shouldn't worry about this unless you want to train a new model.
  • device is the cuda device that will be used for scanning. cpu is not supported as of now.

Here's an example command line.

scan.py /home/user/videos weights config.json cuda:0

On windows you can use scan.bat file with just the video folder as input.

Training

Training scripts with a subset of DFDC dataset will be published soon. Stay tuned! 🔔

Comments
  • CPU code returns 0.5 for all videos with warning message

    CPU code returns 0.5 for all videos with warning message

    CUDA code retuns more realistic values without any warnings.

    Maybe

    252 except Exception as e:
    253    print(e, file, file=sys.stderr)
    254    return 0.5
    

    should be changed.

  • Any reference on funtion label_spread() in strategy()?

    Any reference on funtion label_spread() in strategy()?

    In function strategy(), there's another function label_spread(). I don't understand the mechanism and can't find any reference. Can you point any to me? Thanks a lot!

  • Threshold to determine a video as fake?

    Threshold to determine a video as fake?

    Hi! I use the scanner on deepware.ai to scan a few videos and the result is fantastic. When I run it by using this git, it returns probability. How can I make it return something similar like your website scanner ? Or specifically, what threshold should I take to say a video is fake or not ? I uploaded a fake video that I created. Scanner predicted it right (deepfake detected). But when I run by command, it returns about 0.3 as probability. I'm a little confused here. Thanks!

  • better readability and one bug fix

    better readability and one bug fix

    Replaced manual dicts with defaultdicts to save lines and for better readability. Bug fix: Change len(bad) to len(bad[0]) if i'm not misunderstanding it.

  • Deepware is failing

    Deepware is failing

    Hi, I just try Deepware fake detector today using a real video of myself using the cam of my cellphone with no manipulations of the video of any kind, and for my surprise Deepware said it is a deepfake... so I am wondering if deepfake detectors are actually a real tool to detect fake videos, because if it detect a real video as a fake then there is no sense in use Deepware to detect fake videos

  • Pre-trained models

    Pre-trained models

    Thank you for the wonderful work!!! For the public repo, there is only one pre-trained model. Want to ask if you can also provide the other models used in your online demo or any pointer to obatin? Many thx!

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