jonasricker/diffusion-model-deepfake-detection

[VISAPP2024] Towards the Detection of Diffusion Model Deepfakes

[VISAPP2024] Towards the Detection of Diffusion Model Deepfakes

Jonas Ricker, Simon Damm, Thorsten Holz, Asja Fischer
International Conference on Computer Vision Theory and Applications (VISAPP), 2024

The extended version of our work with additional experiments and supplemental material is available on arxiv.

Code

We provide the source code and instructions on how to recreate the results in the paper. The code is tested with Python 3.8. To install the required packages run pip install -r requirements.txt. You probably should install the version of PyTorch matching your system.

The commands below expect a working directory which contains data, models, and to which results will be written. To run the commandas as-is, save the path to this directory in a variable by executing WORKDIR=path/to/working/directory.

Instructions for downloading the Checkpoints and Dataset are given in the corresponding sections. Your working directory should have the following structure:

workdir/
├── data
│   └── diffusion_model_deepfakes_lsun_bedroom
│       ├── test
│       │   ├── ADM
│       │   ├── ...
│       ├── train  # only required for training your own models
│       │   ├── ADM
│       │   ├── ...
│       └── val  # only required for training your own models
│           ├── ADM
│           ├── ...
└── models
    ├── gragnaniello2021
    │   ├── gandetection_resnet50nodown_progan.pth
    │   └── gandetection_resnet50nodown_stylegan2.pth
    ├── mandelli2022
    │   ├── method_A.pth
    │   ├── method_B.pth
    │   ├── method_C.pth
    │   ├── method_D.pth
    │   └── method_E.pth
    └── wang2020
        ├── blur_jpg_prob0.1.pth
        ├── blur_jpg_prob0.5.pth
        ├── finetuning
        │   ├── ADM
        │   │   └── model_epoch_best.pth
        │   ├── ...
        └── scratch
            ├── ADM
            │   └── model_epoch_best.pth
            ├── ...

Calling any of the commands below with -h prints further information on the arguments.

Detector Evaluation

To recreate the results in Table 2 run

python evaluate_detectors.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/models $WORKDIR/output --img-dirs Real ProGAN StyleGAN ProjectedGAN Diff-StyleGAN2 Diff-ProjectedGAN DDPM IDDPM ADM PNDM LDM --predictors wang2020 gragnaniello2021 mandelli2022 --wang2020-model-path wang2020/blur_jpg_prob0.5.pth wang2020/blur_jpg_prob0.1.pth --gragnaniello2021-model-path gragnaniello2021/gandetection_resnet50nodown_progan.pth gragnaniello2021/gandetection_resnet50nodown_stylegan2.pth --metric AUROC PD@5% PD@1%

To recreate Figures 1a and 1b run

python evaluate_detectors.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/models $WORKDIR/output --img-dirs Real ProGAN StyleGAN ProjectedGAN Diff-StyleGAN2 Diff-ProjectedGAN DDPM IDDPM ADM PNDM LDM --experiment finetuning --predictors wang2020 --wang2020-model-path wang2020/finetuning/ProGAN wang2020/finetuning/StyleGAN wang2020/finetuning/ProjectedGAN wang2020/finetuning/Diff-StyleGAN2 wang2020/finetuning/Diff-ProjectedGAN wang2020/finetuning/DDPM wang2020/finetuning/IDDPM wang2020/finetuning/ADM wang2020/finetuning/PNDM wang2020/finetuning/LDM wang2020/finetuning/GAN wang2020/finetuning/DM wang2020/finetuning/All --output cfm --metric AUROC
python evaluate_detectors.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/models $WORKDIR/output --img-dirs Real ProGAN StyleGAN ProjectedGAN Diff-StyleGAN2 Diff-ProjectedGAN DDPM IDDPM ADM PNDM LDM --experiment finetuning --predictors wang2020 --wang2020-model-path wang2020/finetuning/ProGAN wang2020/finetuning/StyleGAN wang2020/finetuning/ProjectedGAN wang2020/finetuning/Diff-StyleGAN2 wang2020/finetuning/Diff-ProjectedGAN wang2020/finetuning/DDPM wang2020/finetuning/IDDPM wang2020/finetuning/ADM wang2020/finetuning/PNDM wang2020/finetuning/LDM wang2020/finetuning/GAN wang2020/finetuning/DM wang2020/finetuning/All --output cfm --metric PD@1%

To recreate Figures 8a and 8b run

python evaluate_detectors.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/models $WORKDIR/output --img-dirs Real ProGAN StyleGAN ProjectedGAN Diff-StyleGAN2 Diff-ProjectedGAN DDPM IDDPM ADM PNDM LDM --experiment scratch --predictors wang2020 --wang2020-model-path wang2020/scratch/ProGAN wang2020/scratch/StyleGAN wang2020/scratch/ProjectedGAN wang2020/scratch/Diff-StyleGAN2 wang2020/scratch/Diff-ProjectedGAN wang2020/scratch/DDPM wang2020/scratch/IDDPM wang2020/scratch/ADM wang2020/scratch/PNDM wang2020/scratch/LDM wang2020/scratch/GAN wang2020/scratch/DM wang2020/scratch/All --output cfm --metric AUROC
python evaluate_detectors.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/models $WORKDIR/output --img-dirs Real ProGAN StyleGAN ProjectedGAN Diff-StyleGAN2 Diff-ProjectedGAN DDPM IDDPM ADM PNDM LDM --experiment scratch --predictors wang2020 --wang2020-model-path wang2020/scratch/ProGAN wang2020/scratch/StyleGAN wang2020/scratch/ProjectedGAN wang2020/scratch/Diff-StyleGAN2 wang2020/scratch/Diff-ProjectedGAN wang2020/scratch/DDPM wang2020/scratch/IDDPM wang2020/scratch/ADM wang2020/scratch/PNDM wang2020/scratch/LDM wang2020/scratch/GAN wang2020/scratch/DM wang2020/scratch/All --output cfm --metric PD@1%

To recreate the results in Tables 4 and 5 run

python evaluate_detectors.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/models $WORKDIR/output --img-dirs Real ProGAN StyleGAN ProjectedGAN Diff-StyleGAN2 Diff-ProjectedGAN DDPM IDDPM ADM PNDM LDM --experiment noise --predictors gragnaniello2021 --gragnaniello2021-model-path gragnaniello2021/gandetection_resnet50nodown_progan.pth --metric AUROC PD@5% PD@1% --perturbation noise
python evaluate_detectors.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/models $WORKDIR/output --img-dirs Real ProGAN StyleGAN ProjectedGAN Diff-StyleGAN2 Diff-ProjectedGAN DDPM IDDPM ADM PNDM LDM --experiment blur --predictors gragnaniello2021 --gragnaniello2021-model-path gragnaniello2021/gandetection_resnet50nodown_progan.pth --metric AUROC PD@5% PD@1% --perturbation blur
python evaluate_detectors.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/models $WORKDIR/output --img-dirs Real ProGAN StyleGAN ProjectedGAN Diff-StyleGAN2 Diff-ProjectedGAN DDPM IDDPM ADM PNDM LDM --experiment jpeg --predictors gragnaniello2021 --gragnaniello2021-model-path gragnaniello2021/gandetection_resnet50nodown_progan.pth --metric AUROC PD@5% PD@1% --perturbation jpeg
python evaluate_detectors.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/models $WORKDIR/output --img-dirs Real ProGAN StyleGAN ProjectedGAN Diff-StyleGAN2 Diff-ProjectedGAN DDPM IDDPM ADM PNDM LDM --experiment cropping --predictors gragnaniello2021 --gragnaniello2021-model-path gragnaniello2021/gandetection_resnet50nodown_progan.pth --metric AUROC PD@5% PD@1% --perturbation cropping

python evaluate_detectors.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/models $WORKDIR/output --img-dirs Real DDPM IDDPM ADM PNDM LDM --experiment noise_ft_DM --predictors wang2020 --wang2020-model-path wang2020/finetuning/DM --metric AUROC PD@5% PD@1% --perturbation noise
python evaluate_detectors.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/models $WORKDIR/output --img-dirs Real DDPM IDDPM ADM PNDM LDM --experiment blur_ft_DM --predictors wang2020 --wang2020-model-path wang2020/finetuning/DM --metric AUROC PD@5% PD@1% --perturbation blur
python evaluate_detectors.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/models $WORKDIR/output --img-dirs Real DDPM IDDPM ADM PNDM LDM --experiment jpeg_ft_DM --predictors wang2020 --wang2020-model-path wang2020/finetuning/DM --metric AUROC PD@5% PD@1% --perturbation jpeg
python evaluate_detectors.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/models $WORKDIR/output --img-dirs Real DDPM IDDPM ADM PNDM LDM --experiment cropping_ft_DM --predictors wang2020 --wang2020-model-path wang2020/finetuning/DM --metric AUROC PD@5% PD@1% --perturbation cropping

Feature Space Analysis

To extract the features from Gragnaniello2021 run

python detectors/gragnaniello2021/features.py --weights-path $WORKDIR/models/gragnaniello2021/gandetection_resnet50nodown_progan.pth --img-root $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test --output-root $WORKDIR/output
python detectors/gragnaniello2021/features.py --weights-path $WORKDIR/models/gragnaniello2021/gandetection_resnet50nodown_stylegan2.pth --img-root $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test --output-root $WORKDIR/output

To extract the features from Wang2020 using a modified version of the original script run

python detectors/wang2020/demo_dir_features.py --dir $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test --model_path $WORKDIR/models/wang2020/blur_jpg_prob0.1.pth --output_root $WORKDIR/output
python detectors/wang2020/demo_dir_features.py --dir $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test --model_path $WORKDIR/models/wang2020/blur_jpg_prob0.5.pth --output_root $WORKDIR/output
python detectors/wang2020/demo_dir_features.py --dir $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test --model_path $WORKDIR/models/wang2020/finetuning/All --output_root $WORKDIR/output
python detectors/wang2020/demo_dir_features.py --dir $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test --model_path $WORKDIR/models/wang2020/finetuning/GAN --output_root $WORKDIR/output
python detectors/wang2020/demo_dir_features.py --dir $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test --model_path $WORKDIR/models/wang2020/finetuning/DM --output_root $WORKDIR/output
python detectors/wang2020/demo_dir_features.py --dir $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test --model_path $WORKDIR/models/wang2020/scratch/All --output_root $WORKDIR/output
python detectors/wang2020/demo_dir_features.py --dir $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test --model_path $WORKDIR/models/wang2020/scratch/GAN --output_root $WORKDIR/output
python detectors/wang2020/demo_dir_features.py --dir $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test --model_path $WORKDIR/models/wang2020/scratch/DM --output_root $WORKDIR/output

To calculate the Maximum Mean Discrepancy (MMD) based on extracted features run

python feature_space_analysis/calculate_MMD.py -b $WORKDIR/output/features

The plots can then be recreated using the notebooks plot_MMD.ipynb and plot_tSNE.ipynb.

Frequency Analysis

To recreate Figures 3a and 3b run

python frequency_analysis.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/output fft_hp --img-dirs Real ProGAN StyleGAN ProjectedGAN Diff-StyleGAN2 Diff-ProjectedGAN --log --vmin 1e-5 --vmax 1e-1
python frequency_analysis.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/output fft_hp --img-dirs Real DDPM IDDPM ADM PNDM LDM --log --vmin 1e-5 --vmax 1e-1

To compute DCT spectra, as in Figure 14, replace fft_hp with dct.

To recreate Figures 4a and 4b run

python frequency_analysis.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/output density --img-dirs Real ProGAN StyleGAN ProjectedGAN Diff-StyleGAN2 Diff-ProjectedGAN --log --fraction 0.49 --zoom
python frequency_analysis.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/output density --img-dirs Real DDPM IDDPM ADM PNDM LDM --log --fraction 0.49 --zoom

To recreate the results in Table 3 run

python logistic_regression.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom $WORKDIR/output --img-dirs ProGAN StyleGAN ProjectedGAN Diff-StyleGAN2 Diff-ProjectedGAN DDPM IDDPM ADM PNDM LDM

Note that to run this evaluation it is required to also have the train and val directories of the dataset.

Spectrum Evolutions

Since we analyze the spectrum evolution using ADM, it is necessary to clone the repository and install the package by calling pip install . from within. Also, download the LSUN Bedroom checkpoint and save it to $WORKDIR/models/dhariwal2021/lsun_bedroom.pt.

First, compute the reference spectrum from 10k real images by running

python spectrum_evolutions.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test/Real $WORKDIR/output compute_reference lsun_bedroom_ref

To generate samples at different steps of the denoising process run

python sample_adm_denoising_process.py --model $WORKDIR/models/dhariwal2021/lsun_bedroom.pt --output_dir $WORKDIR/output/spectrum_vs_steps --store_within True --stop 1000 --step 10 --timestep_respacing 1000 --attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.1 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True
python sample_adm_denoising_process.py --model $WORKDIR/models/dhariwal2021/lsun_bedroom.pt --output_dir $WORKDIR/output/spectrum_vs_steps --store_within True --stop 100 --step 1 --timestep_respacing 1000 --attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.1 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True

To recreate Figures 5a and 5b run

python spectrum_evolutions.py $WORKDIR/output/spectrum_vs_steps/denoising-1000_10 $WORKDIR/output --fraction 0.49 spectrum_vs_steps $WORKDIR/output/spectrum_vs_steps/lsun_bedroom_ref-10000_samples.npy
python spectrum_evolutions.py $WORKDIR/output/spectrum_vs_steps/denoising-100_1 $WORKDIR/output --fraction 0.49 spectrum_vs_steps $WORKDIR/output/spectrum_vs_steps/lsun_bedroom_ref-10000_samples.npy

To plot the spectrum deviation relative to the diffusion process instead of the real spectrum, we first need to generate samples at different steps of the diffusion process by running

python sample_adm_diffusion_process.py --data_dir $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test/Real --output_dir $WORKDIR/output/spectrum_vs_steps --stop 1000 --step 10 --timestep_respacing 1000 --attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.1 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True
python sample_adm_diffusion_process.py --data_dir $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test/Real --output_dir $WORKDIR/output/spectrum_vs_steps --stop 100 --step 1 --timestep_respacing 1000 --attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.1 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True

Then, to recreate Figures 15a and 15b run

python spectrum_evolutions.py $WORKDIR/output/spectrum_vs_steps/denoising-1000_10 $WORKDIR/output --fraction 0.49 denoising_vs_diffusion $WORKDIR/output/spectrum_vs_steps/diffusion-1000_10
python spectrum_evolutions.py $WORKDIR/output/spectrum_vs_steps/denoising-100_1 $WORKDIR/output --fraction 0.49 denoising_vs_diffusion $WORKDIR/output/spectrum_vs_steps/diffusion-100_1

To recreate Figure 16 run

python spectrum_evolutions.py $WORKDIR/output/spectrum_vs_steps/denoising-100_1 $WORKDIR/output --fraction 0.49  classification $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test/Real

To sample images using different numbers of sampling steps (without and with DDIM) run

for step in 2 4 8 16 25 50 100 200 500 1000; do python sample_adm_denoising_process.py --model $WORKDIR/models/dhariwal2021/lsun_bedroom.pt --output_dir $WORKDIR/output/spectrum_vs_steps --timestep_respacing $step --attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.1 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True; done
for step in 2 4 8 16 25 50 100 200 500 1000; do python sample_adm_denoising_process.py --model $WORKDIR/models/dhariwal2021/lsun_bedroom.pt --output_dir $WORKDIR/output/spectrum_vs_steps --use_ddim True --timestep_respacing $step --attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.1 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True; done

Then, to recreate Figures 18a and 18b run

python spectrum_evolutions.py $WORKDIR/output/spectrum_vs_steps/sampling_steps $WORKDIR/output --fraction 0.49 --ylabel "Denoising Steps" --all-labels spectrum_vs_steps $WORKDIR/output/spectrum_vs_steps/lsun_bedroom_ref-10000_samples.npy
python spectrum_evolutions.py $WORKDIR/output/spectrum_vs_steps/sampling_steps_ddim $WORKDIR/output --fraction 0.49 --ylabel "Denoising Steps" --all-labels spectrum_vs_steps $WORKDIR/output/spectrum_vs_steps/lsun_bedroom_ref-10000_samples.npy

To recreate Figures 19a and 19b run

python spectrum_evolutions.py $WORKDIR/output/spectrum_vs_steps/sampling_steps $WORKDIR/output --fraction 0.49 --ylabel "Denoising Steps" --all-labels classification $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test/Real
python spectrum_evolutions.py $WORKDIR/output/spectrum_vs_steps/sampling_steps_ddim $WORKDIR/output --fraction 0.49 --ylabel "Denoising Steps" --all-labels classification $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test/Real

Fakeness Ratings

To recreate Figures 11 and 12 run

python fakeness.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/output/evaluate/cache $WORKDIR/output --img-dirs ADM DDPM Diff-ProjectedGAN Diff-StyleGAN2 IDDPM LDM PNDM ProGAN ProjectedGAN StyleGAN
python fakeness.py $WORKDIR/data/diffusion_model_deepfakes_lsun_bedroom/test $WORKDIR/output/evaluate/cache $WORKDIR/output --img-dirs ADM DDPM IDDPM LDM PNDM --detector wang2020_finetuning_DM

Note that this script uses the cache generated by evaluate_detectors.py, which is why it needs to be called first.

Checkpoints

Pre-trained models from Wang2020, Gragnaniello2021, and Mandelli2022 can be downloaded from the corresponding repositories.

Checkpoints for the models we trained ourselves using the architecture from Wang2020 are hosted on Zenodo. You can re-create our checkpoints using our Dataset and the instructions from the original repository.

Dataset

The main dataset used in this work is hosted on Zenodo. In total, the dataset contains 50k samples (256x256) for each of the following generators trained on LSUN Bedroom, divided into train, validation, and test set (39k/1k/10k).

GANs	DMs
ProGAN	DDPM
StyleGAN	IDDPM
ProjectedGAN	ADM
Diffusion-StyleGAN2	PNDM
Diffusion-ProjectedGAN	LDM

The test set additionally contains 10k real images from LSUN Bedroom and can be used as-is. Due to storage limitations, the train and validation set contain only generated images. Real images, required for training, have to be added according to the instructions given below.

Test Set

The test set can be downloaded from Zenodo. To validate that all images were downloaded correctly, you can run the following command and compare the hash value (computation takes some time).

$ find diffusion_model_deepfakes_lsun_bedroom/test/ -type f -print0 | sort -z | xargs -0 sha1sum | cut -d " " -f 1 | sha1sum
68bd7326113b9cde3cd4eadf1e10e5fca77df751  -

Train and Validation Set

To recreate the train and validation set, first download both zip-archives from Zenodo and extract them in the same directory. Then, download the entire LSUN Bedroom training data in LMDB format according to the instructions in the LSUN repository. Finally, run recreate_train_data.py (requires packages lmbd and Pillow):

$ python recreate_train_data.py path/to/bedroom_train_lmbd path/to/diffusion_model_deepfakes_lsun_bedroom

The script will distribute real images such that for each generator there is a unique set of 40k real images (39k train, 1k validation) with the correct dimensions. Note that due to the large amount of images this will take some time. For each generator, real images will be in 0_real, fake images in 1_fake.

The script performs a simple form of validation. You can additionally run the following commands and compare the hash values (computation takes some time).

$ find diffusion_model_deepfakes_lsun_bedroom/train/ -type f -print0 | sort -z | xargs -0 sha1sum | cut -d " " -f 1 | sha1sum
10fd255e5669e76b4e9bde93f672733a5dfa92fa  -

$ find diffusion_model_deepfakes_lsun_bedroom/val/ -type f -print0 | sort -z | xargs -0 sha1sum | cut -d " " -f 1 | sha1sum
c836fe592e49ab630ccc30675694ab32aff8e1a3  -

Additional Datasets

The additional datasets (LSUN Cat, LSUN Horse, LSUN Church, ImageNet, FFHQ, Text2Img) are hosted on Zenodo. For each dataset we provide real and generated images from the generators specified in the paper. An exception is Text2Img, for which we cannot provide real images due to copyright.

diffusion-model-deepfake-detection's Issues

about datasets

Hi jonasricker!
I wonder if the generated face dataset is included in the dataset you provided.

fix a problem "ModuleNotFoundError: No module named 'seaborn._oldcore'"

seaborn._oldcore is unavailable anymore, so it reports the above error.
I suggest changing the code of line 107-108 in the file " src/paper_utils.py" to "dashes = ["-"]*len(sns.color_palette("deep"))".

run on cpu

Hello there!
I have a problem running yours scripts because I have Macbook with Intel Chip so I cannot install torch with CUDA )))) So scrips falls with following error:
AssertionError: Torch not compiled with CUDA enabled
is there any simple way to run scripts on cpu? maybe it's very simple, but i'm new to nn and python, so i can't find easy way to do it

Thanks)

related work weights

Hi,

thanks for providing the useful code. https://github.com/jonasricker/diffusion-model-deepfake-detection/tree/main/detectors

I am wondering if you provide your own trained weights on Diffusion Datasets or if these weights are the original one?

about datasets

when the image is jpg,can it run?

LDM and PNDM just have 40k samples each over test,train,val

Hi,

I have been in the process of downloading all the images from the zenodo link and I wanted to create a folder of 50k items for each of the models, so I can use them for my own experiments. However, I have noticed that LDM and PNDM only have 40k samples in total after combining all folders. I suspect this might be due to the train zip only containing 29k samples for LDM and PNDM, but I cannot say this for sure, as I do not have access to that zip anymore and downloading it again will take a long time.

Is this true or is it a mistake on my part?

Requirements

Hi jonasricker
I want to test your model and get your result but i have a problem with packages that code need.
Can you tell me version of each package you used?
I will be grateful.