DeepFake Detector

DeepFake Detector (DFD) is a CLI program used to test how well one of supported DeepFake detection methods¹ differentiate DeepFake's from images altered by common image modification methods such as gamma correction. This tool was used in my bachelor thesis Testing robustness of DeepFake recognition methods against non-malicious image modification

Table of contents

• What's the point?
• What is this?
• How it works?
  • TL;DR:
  • Tell me more!
• Design
• Installation
• GPU configuration

What's the point?

Videos can be used as evidences in judicial cases and police investigations. In such scenario it is crucial for DeepFake recognition method used to properly discriminate DeepFake from authentic video whose brightness was adjusted.

What is this?

• CLI program allowing to:
  • Preprocess dataset containing real and fake videos. Chosen part of negatives is modified by selected non-malicious image modifications based on settings assigned by user.
    • Preprocessing can be done via single command or gradually in steps where single step represents activity such as extract faces from fake videos. The letter method allows preprocessing large datasets² on without the need of keeping program on for 24+ hours.
  • Train detection model and evaluate it in assigned settings.

How it works?

TL;DR:

Assuming that INPUT points to directory containing two sub-directories:

• reals -> containing authentic videos
• fakes -> containing DeepFakes The following commands can be used to train and evaluate Meso-4 model in default settings³:

# Preprocess data . More advanced users can optionally split this command
# into smaller steps , such as face extraction from negatives .
# This can be utilized while working with large datasets such as Celeb - DF .
dfd preprocess ${INPUT} ${OUTPUT} dataset ${STORAGE}
dfd train --output-path ${MODEL} ${OUTPUT}/train ${OUTPUT}/validation
dfd test ${MODEL} ${OUTPUT} / test
dfd predict MODEL ${DATA_SAMPLE}

Tell me more!

To list possible commands use dfd --help. Most configurable params use reasonable defaults. The modifications used to alter negatives are configured via YAML files. Here is a sample:

---
# Example modifications settings.
# In total half of negatives are altered.
modifications:
  - name: RedEyesEffectModification
  share: 0.125
  options:
    brightness_threshold: 50
  - name: CLAHEModification
  share: 0.125
  options:
    clip_limit: 2.0
    grid_width: 8
    grid_height: 8
  - name: HistogramEqualizationModification
  share: 0.125
  - name: GaussianBlurModification
  share: 0.125
  options:
    kernel_width: 9
    kernel_height: 9

The names of supported modifications can be found in this file.

Design

The application design is loosely inspired by Hexagonal Architecture pattern . Since DFD is relatively simple, Hexagonal Architecture pattern is not fully embraced. Instead, two components corresponding to ports are defined: ModificationRegistry and ModelRegistry. The concrete implementations of ML models and image modifications play the roles of adapters plugged to their respective ports.

Installation

• TL;DR:

pip install git+https://github.com/cicheck/dfd.git

• The program is tested under Ubuntu 20.04.3, however the use of tox in testing process should ensure that the package is largely independent of tested system and the installation process is similar for all major recent releases of Windows or Unix like system.
• Requirements:
  • Python version 3.9.X
• Recommendations:
  • It is strongly recommended to use Python's virtual environments to avoid package version clashes.

GPU configuration

• The program can be run without CUDA configured however if the GPU is enabled it might significantly speed up computations.
• The application uses TensorFlow 2.5.0, which for GPU based computations requires:
  • CUDA Toolkit 11.2
  • NVIDIA GPU driver 450.80.02
  • CUPTI

Footnotes

1. Currently the only supported detection method is Meso-4. ↩

2. Such as Celeb-DF. ↩

3. Half of negatives modified, 4 modifications used with naive parameters. ↩