FatFormer

This repository is an official implementation of the CVPR 2024 paper "Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection".

☀️ If you find this work useful for your research, please kindly star our repo and cite our paper! ☀️

TODO

We are working hard on the following items.

• Release arXiv paper
• Release inference scripts
• Release checkpoints
• Release datasets

Introduction

In this paper, we study the problem of generalizable synthetic image detection, e.g., GANs and diffusion models. Cutting-edge solutions start to explore the benefits of pre-trained models, and mainly follow the fixed paradigm of solely training an attached classifier. However, our analysis shows that such a fixed paradigm is prone to yield detectors with insufficient learning regarding forgery representations. We attribute the key challenge to the lack of forgery adaptation, and present a novel forgery-aware adaptive transformer approach, namely FatFormer.

Based on the pre-trained vision-language spaces of CLIP, FatFormer introduces two core designs for the adaption to build generalized forgery representations. First, motivated by the fact that both image and frequency analysis are essential for synthetic image detection, we develop a forgery-aware adapter to adapt image features to discern and integrate local forgery traces within image and frequency domains. Second, we find that considering the contrastive objectives between adapted image features and text prompt embeddings, a previously overlooked aspect, results in a nontrivial generalization improvement. Accordingly, we introduce language-guided alignment to supervise the forgery adaptation with image and text prompts in FatFormer. Experiments show that, by coupling these two designs, our approach tuned on 4-class ProGAN data attains a remarkable detection performance, achieving an average of 98% accuracy to unseen GANs, and surprisingly generalizes to unseen diffusion models with 95% accuracy.

Model Zoo

Checkpoint can be found here(baidu & onedrive & Google Drive).

Results on the GANs dataset (4-class supervision)

Methods	Ref	ProGAN	StyleGAN	StyleGAN2	BigGAN	CycleGAN	StarGAN	GauGAN	Deepfake	Mean
Wang [48]	CVPR 2020	91.4 / 99.4	63.8 / 91.4	76.4 / 97.5	52.9 / 73.3	72.7 / 88.6	63.8 / 90.8	63.9 / 92.2	51.7 / 62.3	67.1 / 86.9
Durall [11]	CVPR 2020	81.1 / 74.4	54.4 / 52.6	66.8 / 62.0	60.1 / 56.3	69.0 / 64.0	98.1 / 98.1	61.9 / 57.4	50.2 / 50.0	67.7 / 64.4
Frank [12]	ICML 2020	90.3 / 85.2	74.5 / 72.0	73.1 / 71.4	88.7 / 86.0	75.5 / 71.2	99.5 / 99.5	69.2 / 77.4	60.7 / 49.1	78.9 / 76.5
PatchFor [3]	ECCV 2020	97.8 / 100.0	82.6 / 93.1	83.6 / 98.5	64.7 / 69.5	74.5 / 87.2	100.0 / 100.0	57.2 / 55.4	85.0 / 93.2	80.7 / 87.1
F3Net [37]	ECCV 2020	99.4 / 100.0	92.6 / 99.7	88.0 / 99.8	65.3 / 69.9	76.4 / 84.3	100.0 / 100.0	58.1 / 56.7	63.5 / 78.8	80.4 / 86.2
Blend† [45]	CVPR 2022	58.8 / 65.2	50.1 / 47.7	48.6 / 47.4	51.1 / 51.9	59.2 / 65.3	74.5 / 89.2	59.2 / 65.5	93.8 / 99.3	61.9 / 66.4
BiHPF [20]	WACV 2022	90.7 / 86.2	76.9 / 75.1	76.2 / 74.7	84.9 / 81.7	81.9 / 78.9	94.4 / 94.4	69.5 / 78.1	54.4 / 54.6	78.6 / 77.9
FrePGAN [21]	AAAI 2022	99.0 / 99.9	80.7 / 89.6	84.1 / 98.6	69.2 / 71.1	71.1 / 74.4	99.9 / 100.0	60.3 / 71.7	70.9 / 91.9	79.4 / 87.2
LGrad [46]	CVPR 2023	99.9 / 100.0	94.8 / 99.9	96.0 / 99.6	82.9 / 90.7	85.3 / 94.0	99.6 / 100.0	72.4 / 79.3	58.0 / 67.9	86.1 / 91.5
UniFD [35]	CVPR 2023	99.7 / 100.0	89.0 / 98.7	83.9 / 98.4	90.5 / 99.1	87.9 / 99.8	91.4 / 100.0	89.9 / 100.0	80.2 / 90.2	89.1 / 98.3
Ours	CVPR 2024	99.9 / 100.0	97.2 / 99.8	98.8 / 100.0	99.5 / 100.0	99.3 / 100.0	99.8 / 100.0	99.4 / 100.0	93.2 / 98.0	98.4 / 99.7

Results on the DMs dataset (4-class supervision)

Dataset	Wang [48]	Durall [11]	Frank [12]	PatchFor [3]	F3Net [37]	Blend† [45]	LGrad [46]	UniFD [35]	Ours
PNDM	50.8 / 90.3	44.5 / 47.3	44.0 / 38.2	50.2 / 99.9	72.8 / 99.5	48.2 / 48.1	69.8 / 98.5	75.3 / 92.5	99.3 / 100.0
Guided	54.9 / 66.6	40.6 / 42.3	53.4 / 52.5	74.2 / 81.4	69.2 / 70.8	58.3 / 63.4	86.6 / 100.0	75.7 / 85.1	76.1 / 92.0
DALL-E	51.8 / 61.3	55.9 / 58.0	57.0 / 62.5	79.8 / 99.1	71.6 / 79.9	52.4 / 51.6	88.5 / 97.3	89.5 / 96.8	98.8 / 99.8
VQ-Diffusion	50.0 / 71.0	38.6 / 38.3	51.7 / 66.7	100.0 / 100.0	100.0 / 100.0	77.1 / 82.6	96.3 / 100.0	83.5 / 97.7	100.0 / 100.0
LDM_200 steps	52.0 / 64.5	61.7 / 61.7	56.4 / 50.9	95.6 / 99.9	73.4 / 83.3	52.6 / 51.9	94.2 / 99.1	90.2 / 97.1	98.6 / 99.8
LDM_200 w/ CFG	51.6 / 63.1	58.4 / 58.5	56.5 / 52.1	94.0 / 99.8	80.7 / 89.1	51.9 / 52.6	95.9 / 99.2	77.3 / 88.6	94.9 / 99.1
LDM_100 steps	51.9 / 63.7	62.0 / 62.6	56.6 / 51.3	95.8 / 99.8	74.1 / 84.0	53.0 / 54.0	94.8 / 99.0	90.5 / 97.0	98.7 / 99.9
Glide_100-27	53.0 / 71.3	48.9 / 46.9	50.4 / 40.8	82.8 / 99.1	87.0 / 94.5	59.4 / 64.1	87.4 / 95.2	90.7 / 95.8	94.4 / 99.1
Glide_50-27	54.2 / 76.0	51.7 / 49.9	52.0 / 42.3	84.9 / 98.8	88.5 / 95.4	64.2 / 68.3	90.7 / 97.2	91.1 / 97.4	94.7 / 99.4
Glide_100-10	53.3 / 72.9	54.9 / 52.3	53.6 / 44.3	87.3 / 99.7	88.3 / 95.4	58.8 / 63.2	89.4 / 99.0	90.1 / 97.4	94.2 / 99.2
Mean	52.4 / 70.1	51.7 / 51.8	53.2 / 50.2	84.5 / 97.8	80.6 / 89.2	57.6 / 60.0	89.4 / 97.7	85.4 / 94.6	95.0 / 98.8

Datasets

Training data

To train FatFormer, we adopt images generated by ProGAN with two training settings, consisting of 2-class (chair, horse) and 4-class (car, cat, chair, horse), following CNNDetection. The original download link can be found in here. You can also download it from our mirror site.

Testing data

To evaluate FatFormer, we consider the synthetic images from both GANs and diffusion models (DMs).

• GANs dataset

  For the GANs dataset, we utilize the 8 types of GANs for testing, including ProGAN, StyleGAN, StyleGAN2, BigGAN, CycleGAN, StarGAN, GauGAN and DeepFake, following LGrad. The original download link can be found here. You can also download from our baidu mirror site and onedrive mirror site.

• DMs dataset

  For the DMs dataset, we collect 6 types of SOTA DMs, including PNDM, Guided, DALL-E, VQ-Diffusion, LDM, and Glide, from DIRE and UniversalFakeDetect. The original download link can be found here and here. You can also download from our baidu mirror site and onedrive mirror site.

Data Folder Formulation

We expect the directory structure to be the following:

path/to/dataset/
├── train/
|   ├── car/ # sub-category of ProGAN
|   |   ├── 0_real # real images
|   |   └── 1_fake # fake images
|   └── ...
└── test/
    ├── AttGAN/ # testing generators
    |   ├── 0_real # real images
    |   └── 1_fake # fake images
    └── ...   

Installation

Requirements

The code is developed and validated with python=3.7.10,pytorch=1.7.1,cuda=11.0. Higher versions might be as well.

1. Create your own Python environment with Anaconda.

conda create -n fatformer python=3.7.10

2. Activate fatformer environment and install PyTorch, torchvision, and other Python packages.

conda activate fatformer
# pytorch, torchvision
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=11.0 -c pytorch
# others
pip install hashlib pkg_resources tqdm gzip

3. To support frequency analysis, you also need to install the pytorch_wavelets package following the pytorch wavelets instruction.

4. Clone this repo.

git clone https://github.com/Michel-liu/FatFormer.git
cd FatFormer

Inference Guidance

We provide the command to evaluate FatFormer on a single node with 4 gpus.

Evaluating on the GANs dataset

• CLIP:ViT-L/14 as backbone, you need first to download the checkpoint and save it into pretrained folder under the FatFormer code base.

export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m torch.distributed.launch \
    --nproc_per_node=4 \
    --master_port 29579 \
    main.py \
    --dataset_path <path/to/dataset/> \
    --test_selected_subsets 'progan' 'stylegan' 'stylegan2' 'biggan' 'cyclegan' 'stargan' 'gaugan' 'deepfake' \
    --eval \
    --pretrained_model <path/to/ckpt/> \
    --num_vit_adapter 3 \
    --num_context_embedding 8

Evaluating on the DMs dataset

You only need to change the --test_selected_subsets flag with DMs that you want to evaluate.

License

FatFormer is released under the Apache 2.0 license. Please see the LICENSE file for more information.

Acknowledgement

This project is built on the open-source repositories CNNDetection, LGrad, DIRE and UniversalFakeDetect. Thank them for their well-organized codes and datasets!

Citation

@inproceedings{liu2024forgeryaware,
  title       = {Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection},
  author      = {Liu, Huan and Tan, Zichang and Tan, Chuangchuang and Wei, Yunchao and Wang, Jingdong and Zhao, Yao},
  booktitle   = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year        = {2024},
}