An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape (Official repo)

In this repository, we release code, datasets and model checkpoints for the paper --- "An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape" accepted by IEEE S&P 2024.

Datasets and Model checkpoints

To access the datasets and model checkpoints, please fill out the Google Form

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Setup

git clone https://github.com/secml-lab-vt/EvolvingThreat-DeepfakeImageDetect.git
cd EvolvingThreat-DeepfakeImageDetect

conda env create --name env_name --file=env.yml
conda activate env_name

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Defenses we studied

Installation, finetuning and inference instructions for the 8 defenses that we have studied are in defenses folder. Please follow the README file in defenses.

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Sec 5.1.3 : Content-agnostic features

Denoiser

We use the MM-BSN [CVPRW 2023] denoiser to get denoised images. Follow instructions in original repo for installation.

To denoise an image, run the following:

python test.py -c SIDD -g 0 --pretrained ./ckpt/SIDD_MMBSN_o_a45.pth --test_dir ./dataset/test_data --save_folder ./outputs/

• pretrained: path to pretrained denoiser model
• test_dir: path to images to be denoised
• save_folder: output path to save the denoised images

Extract Noise

cd contentagnostic
python extractnoise.py --origpath <path to original images> --denpath <path to denoised images> --outputpath <path where image noise will be saved>

Train DCT + Noise features

python traindct_w_noise.py --image_root <path to train images> --noise_image_root <path to noise of train images> --output_path <path to save trained model>

Inference

python testdct_w_noise.py --fake_root <path to test fake> --real_root <path to test real> --noise_fake_root <path to noise of test fake images> --noise_real_root <path to noise of test real images> --model_path <path to trained model> --path_to_mean_std <path to saved mean and std values during training>

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Sec 5.2 : Our Adversarial Attack

Finetuning Surrogate Models

cd surrogatemodels

For finetuning CLIP-ResNet:

python train_clipresnet.py --lr 1e-3 --epochs 30

We provide scripts in surrogatemodels for EfficientNet and ViT finetuning, similar to CLIP-ResNet.

CLIP-ResNet inference:

python infer_clipresnet.py --model_path <path to finetuned model> --input_path <path to test data>

We provide scripts in surrogatemodels for EfficientNet and ViT inference, similar to CLIP-ResNet.

Script for adversarial attack

cd adversarialattack/stylegan2-pytorch

Download the e4e encoder and place it inside adversarialattack/encoder4editing folder.

Run our adversarial attack for the CLIP-ResNet surrogate classifier with the following command:

python adversarialattack_clipresnet.py --inputpath ./dataset/ --savepath ./outputs/ --plosscoeff 1.0 --classifiercoeff 0.1 --alpha 9.0 --beta 0.12 --lr 1e-3

• inputpath: path to input images for adversarial manipulations
• savepath: path to save adversarial images
• plosscoeff: perceptual loss coefficient. We use 1.0 always
• classifiercoeff: classifier loss coefficient. We use 0.1 for EfficientNet and ViT, and 0.02 for CLIP-ResNet
• lr: we use learning rate of 1e-3

Provide finetuned surrogate classifier path accordingly in the script. We also provide similar scripts in adversarialattack/stylegan2-pytorch for running adversarial attack with EfficientNet and ViT surrogate deepfake classifiers.

UnivConv2B defense

cd univconv2B

Finetuning

python train_univconv.py --epochs 30 --lr 1e-3

Inference

python infer_univconv.py --model_path <path to finetuned model> --input_path <path to test data>

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Metrics

KID

Install library

pip install clean-fid

Calculate KID value

cd Metrics
python calcKID.py --dir1 <path to first directory of images> --dir2 <path to second directory of images>

Basically, provide paths to image directories you want to calculate KID for.

CLIP-Score

We followed the instructions from the original repo.

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Cite the paper

@inproceedings{abdullah2024analysis,
  title={An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape},
  author={Abdullah, Sifat Muhammad and Cheruvu, Aravind and Kanchi, Shravya and Chung, Taejoong and Gao, Peng and Jadliwala, Murtuza and Viswanath, Bimal},
  booktitle={Proc. of IEEE S\&P},
  year={2024},
}