README.md

fake-detection-lab

Media Forensics / Fake Image Detection experiments in PyTorch.

Project report can be found here.

Installation

We use conda for managing Python and CUDA versions, and pip-tools for managing Python package dependencies.

1. Specify the appropriate cudatoolkit and cudnn versions to install on your machine in the environment.yml file.
2. To create the conda environment, run: conda env create
3. Activate the environment: conda activate fake-detection-lab
4. Install all necessary packages: pip-sync requirements/prod.txt

Model Artifacts

All model artifacts can be accessed and downloaded here.

• exif_sc.npy: EXIF-SC model weights

Project Structure

├── artifacts
│   └── exif_sc.npy      <-- Store model weights here
├── assets
├── configs              <-- Configuration files for scripts
├── data
│   ├── downloaded       <-- To store downloaded data
│   └── raw              <-- Dataset metadata
├── notebooks
├── requirements
├── src
│   ├── attacks          <-- Implementation of adversarial attacks
│   ├── datasets         <-- Data loading classes
│   ├── evaluation       <-- Evaluation classes and utilities
│   ├── models           <-- Implementation of detection models
│   ├── trainers         <-- Classes for model training
│   ├── structures.py
│   └── utils.py
├── evaluate.py          <-- Main entry point for evaluation
├── non_adv_evaluate.py  <-- Main entry point for evaluation
├── train.py             <-- Main entry point for training
└── ...

Usage

Training

python train.py \
    --config configs/train/exif_sc.yaml \
    --checkpoints_dir checkpoints \
    --gpu 0

Runs training on a dataset, based on the settings specified in the configuration file. Weights are saved as a torch .ckpt file in the specified directory.

More info.

Evaluation

More info here and here

Without Adversarial Attack

python non_adv_evaluate.py \
    --config configs/evaluate/non_adv.yaml \
    --weights_path path/to/weights.{npy, ckpt}

Runs the evaluation on a dataset, based on the settings specified in the configuration file.

With Adversarial Attack

python evaluate.py \
    --config configs/evaluate/adv.yaml \
    --weights_path path/to/weights.{npy, ckpt}

Runs the evaluation on a clean dataset, and also on the dataset after it has been adversarially perturbed, based on the settings specified in the configuration file.

Datasets

All metadata for the datasets used can be found here.

Resources

Model Conversion

• Microsoft's MMdnn
• ONNX

Survey Papers

• Media Forensics and DeepFakes: an overview (Luisa Verdoliva, 2020)
• A Survey of Machine Learning Techniques in Adversarial Image Forensics (Nowroozia et al., 2020)

Fake Detectors

• Fighting Fake News: Image Splice Detection via Learned Self-Consistency (Huh et al., ECCV 2018)

Adversarial Machine Learning

• Adversarial Attack on Deep Learning-Based Splice Localization (Rozsa et al., 2020)