File size: 14,331 Bytes
9e14838 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 | # Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
<p align="center">
<br>
Beijing Jiaotong University, YanShan University, A*Star
</p>
<img src="./NPR.png" width="100%" alt="overall pipeline">
Reference github repository for the paper [Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection](https://arxiv.org/abs/2312.10461).
```
@misc{tan2023rethinking,
title={Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection},
author={Chuangchuang Tan and Huan Liu and Yao Zhao and Shikui Wei and Guanghua Gu and Ping Liu and Yunchao Wei},
year={2023},
eprint={2312.10461},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## News 🆕
- `2024/02`: NPR is accepted by CVPR 2024! Congratulations and thanks to my all co-authors!
- `2024/05`: [🤗Online Demo](https://huggingface.co/spaces/tancc/Generalizable_Deepfake_Detection-NPR-CVPR2024)
<a href="https://huggingface.co/spaces/tancc/Generalizable_Deepfake_Detection-NPR-CVPR2024"><img src="assets/demo_detection.gif" width="70%"></a>
## Environment setup
**Classification environment:**
We recommend installing the required packages by running the command:
```sh
pip install -r requirements.txt
```
In order to ensure the reproducibility of the results, we provide the following suggestions：
- Docker image: nvcr.io/nvidia/tensorflow:21.02-tf1-py3
- Conda environment: [./pytorch18/bin/python](https://drive.google.com/file/d/16MK7KnPebBZx5yeN6jqJ49k7VWbEYQPr/view)
- Random seed during testing period: [Random seed](https://github.com/chuangchuangtan/NPR-DeepfakeDetection/blob/b4e1bfa59ec58542ab5b1e78a3b75b54df67f3b8/test.py#L14)
## Getting the data
<!--
Download dataset from [CNNDetection CVPR2020 (Table1 results)](https://github.com/peterwang512/CNNDetection), [GANGen-Detection (Table2 results)](https://github.com/chuangchuangtan/GANGen-Detection) ([googledrive](https://drive.google.com/drive/folders/11E0Knf9J1qlv2UuTnJSOFUjIIi90czSj?usp=sharing)), [UniversalFakeDetect CVPR2023](https://github.com/Yuheng-Li/UniversalFakeDetect) ([googledrive](https://drive.google.com/drive/folders/1nkCXClC7kFM01_fqmLrVNtnOYEFPtWO-?usp=drive_link)), [DIRE 2023ICCV](https://github.com/ZhendongWang6/DIRE) ([googledrive](https://drive.google.com/drive/folders/1jZE4hg6SxRvKaPYO_yyMeJN_DOcqGMEf?usp=sharing)), Diffusion1kStep [googledrive](https://drive.google.com/drive/folders/14f0vApTLiukiPvIHukHDzLujrvJpDpRq?usp=sharing).
-->
| | paper | Url |
|:----------------------:|:-----:|:-----:|
| Train set | [CNNDetection CVPR2020](https://github.com/PeterWang512/CNNDetection) | [Baidudrive](https://pan.baidu.com/s/1l-rXoVhoc8xJDl20Cdwy4Q?pwd=ft8b) |
| Val set | [CNNDetection CVPR2020](https://github.com/PeterWang512/CNNDetection) | [Baidudrive](https://pan.baidu.com/s/1l-rXoVhoc8xJDl20Cdwy4Q?pwd=ft8b) |
| Table1 Test | [CNNDetection CVPR2020](https://github.com/PeterWang512/CNNDetection) | [Baidudrive](https://pan.baidu.com/s/1l-rXoVhoc8xJDl20Cdwy4Q?pwd=ft8b) |
| Table2 Test | [FreqNet AAAI2024](https://github.com/chuangchuangtan/FreqNet-DeepfakeDetection) | [googledrive](https://drive.google.com/drive/folders/11E0Knf9J1qlv2UuTnJSOFUjIIi90czSj?usp=sharing) |
| Table3 Test | [DIRE ICCV2023](https://github.com/ZhendongWang6/DIRE) | [googledrive](https://drive.google.com/drive/folders/1jZE4hg6SxRvKaPYO_yyMeJN_DOcqGMEf?usp=sharing) |
| Table4 Test | [UniversalFakeDetect CVPR2023](https://github.com/Yuheng-Li/UniversalFakeDetect) | [googledrive](https://drive.google.com/drive/folders/1nkCXClC7kFM01_fqmLrVNtnOYEFPtWO-?usp=sharing)|
| Table5 Test | Diffusion1kStep | [googledrive](https://drive.google.com/drive/folders/14f0vApTLiukiPvIHukHDzLujrvJpDpRq?usp=sharing) |
```
pip install gdown==4.7.1
chmod 777 ./download_dataset.sh
./download_dataset.sh
```
## Directory structure
<details>
<summary> Click to expand the folder tree structure. </summary>
```
datasets
|-- ForenSynths_train_val
| |-- train
| | |-- car
| | |-- cat
| | |-- chair
| | `-- horse
| `-- val
| | |-- car
| | |-- cat
| | |-- chair
| | `-- horse
| |-- test
| |-- biggan
| |-- cyclegan
| |-- deepfake
| |-- gaugan
| |-- progan
| |-- stargan
| |-- stylegan
| `-- stylegan2
`-- Generalization_Test
|-- ForenSynths_test # Table1
| |-- biggan
| |-- cyclegan
| |-- deepfake
| |-- gaugan
| |-- progan
| |-- stargan
| |-- stylegan
| `-- stylegan2
|-- GANGen-Detection # Table2
| |-- AttGAN
| |-- BEGAN
| |-- CramerGAN
| |-- InfoMaxGAN
| |-- MMDGAN
| |-- RelGAN
| |-- S3GAN
| |-- SNGAN
| `-- STGAN
|-- DiffusionForensics # Table3
| |-- adm
| |-- ddpm
| |-- iddpm
| |-- ldm
| |-- pndm
| |-- sdv1_new
| |-- sdv2
| `-- vqdiffusion
`-- UniversalFakeDetect # Table4
| |-- dalle
| |-- glide_100_10
| |-- glide_100_27
| |-- glide_50_27
| |-- guided # Also known as ADM.
| |-- ldm_100
| |-- ldm_200
| `-- ldm_200_cfg
|-- Diffusion1kStep # Table5
|-- DALLE
|-- ddpm
|-- guided-diffusion # Also known as ADM.
|-- improved-diffusion # Also known as IDDPM.
`-- midjourney
```
</details>
## Training the model
```sh
CUDA_VISIBLE_DEVICES=0 ./pytorch18/bin/python train.py --name 4class-resnet-car-cat-chair-horse --dataroot ./datasets/ForenSynths_train_val --classes car,cat,chair,horse --batch_size 32 --delr_freq 10 --lr 0.0002 --niter 50
```
## Testing the detector
Modify the dataroot in test.py.
```sh
CUDA_VISIBLE_DEVICES=0 ./pytorch18/bin/python test.py --model_path ./NPR.pth --batch_size {BS}
```
## Detection Results
### [AIGCDetectBenchmark](https://drive.google.com/drive/folders/1p4ewuAo7d5LbNJ4cKyh10Xl9Fg2yoFOw) using [ProGAN-4class checkpoint](https://github.com/chuangchuangtan/NPR-DeepfakeDetection/blob/main/model_epoch_last_3090.pth)
When testing on AIGCDetectBenchmark, set no_resize and no_crop to True, and set batch_size to 1.
To deal with images of odd sizes, add the following code in [network/resnet.py](https://github.com/chuangchuangtan/NPR-DeepfakeDetection/blob/e2dbbe673c69c0c7237726e809a725a0308ec43d/networks/resnet.py#L163).
```
n,c,w,h = x.shape
if w%2 == 1 : x = x[:,:,:-1,:]
if h%2 == 1 : x = x[:,:,:,:-1]
```
| Generator | CNNSpot | FreDect | Fusing | GramNet | LNP | LGrad | DIRE-G | DIRE-D | UnivFD | RPTCon | NPR |
| :---------:| :-----: |:-------:| :--------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:|:-------:|:----:|
| ProGAN | 100.00 | 99.36 | 100.00 | 99.99 | 99.67 | 99.83 | 95.19 | 52.75 | 99.81 | 100.00 | 99.9 |
| StyleGan | 90.17 | 78.02 | 85.20 | 87.05 | 91.75 | 91.08 | 83.03 | 51.31 | 84.93 | 92.77 | 96.1 |
| BigGAN | 71.17 | 81.97 | 77.40 | 67.33 | 77.75 | 85.62 | 70.12 | 49.70 | 95.08 | 95.80 | 87.3 |
| CycleGAN | 87.62 | 78.77 | 87.00 | 86.07 | 84.10 | 86.94 | 74.19 | 49.58 | 98.33 | 70.17 | 90.3 |
| StarGAN | 94.60 | 94.62 | 97.00 | 95.05 | 99.92 | 99.27 | 95.47 | 46.72 | 95.75 | 99.97 | 99.6 |
| GauGAN | 81.42 | 80.57 | 77.00 | 69.35 | 75.39 | 78.46 | 67.79 | 51.23 | 99.47 | 71.58 | 85.4 |
| Stylegan2 | 86.91 | 66.19 | 83.30 | 87.28 | 94.64 | 85.32 | 75.31 | 51.72 | 74.96 | 89.55 | 98.1 |
| WFIR | 91.65 | 50.75 | 66.80 | 86.80 | 70.85 | 55.70 | 58.05 | 53.30 | 86.90 | 85.80 | 60.7 |
| ADM | 60.39 | 63.42 | 49.00 | 58.61 | 84.73 | 67.15 | 75.78 | 98.25 | 66.87 | 82.17 | 84.9 |
| Glide | 58.07 | 54.13 | 57.20 | 54.50 | 80.52 | 66.11 | 71.75 | 92.42 | 62.46 | 83.79 | 96.7 |
| Midjourney | 51.39 | 45.87 | 52.20 | 50.02 | 65.55 | 65.35 | 58.01 | 89.45 | 56.13 | 90.12 | 92.6 |
| SDv1.4 | 50.57 | 38.79 | 51.00 | 51.70 | 85.55 | 63.02 | 49.74 | 91.24 | 63.66 | 95.38 | 97.4 |
| SDv1.5 | 50.53 | 39.21 | 51.40 | 52.16 | 85.67 | 63.67 | 49.83 | 91.63 | 63.49 | 95.30 | 97.5 |
| VQDM | 56.46 | 77.80 | 55.10 | 52.86 | 74.46 | 72.99 | 53.68 | 91.90 | 85.31 | 88.91 | 90.1 |
| Wukong | 51.03 | 40.30 | 51.70 | 50.76 | 82.06 | 59.55 | 54.46 | 90.90 | 70.93 | 91.07 | 91.7 |
| DALLE2 | 50.45 | 34.70 | 52.80 | 49.25 | 88.75 | 65.45 | 66.48 | 92.45 | 50.75 | 96.60 | 99.6 |
| Average | 70.78 | 64.03 | 68.38 | 68.67 | 83.84 | 75.34 | 68.68 | 71.53 | 78.43 | 89.31 | **91.7** |
### [GenImage](https://github.com/GenImage-Dataset/GenImage)
<details>
<summary> (1) Change "resize" to "translate and duplicate". (2) Set random seed to 70. (3) During testing, set no_crop to False. </summary>
(1)
```
dset = datasets.ImageFolder(
root,
transforms.Compose([
# rz_func,
transforms.Lambda(lambda img: translate_duplicate(img, opt.cropSize)),
crop_func,
flip_func,
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]))
import math
def translate_duplicate(img, cropSize):
if min(img.size) < cropSize:
width, height = img.size
new_width = width * math.ceil(cropSize/width)
new_height = height * math.ceil(cropSize/height)
new_img = Image.new('RGB', (new_width, new_height))
for i in range(0, new_width, width):
for j in range(0, new_height, height):
new_img.paste(img, (i, j))
return new_img
else:
return img
```
(2)
Set [random seed](https://github.com/chuangchuangtan/NPR-DeepfakeDetection/blob/bed4c2c9eb9b2000e9a24233d3b010afa3452f12/train.py#L48) to 70.
(3)
During testing, set [no_crop](https://github.com/chuangchuangtan/NPR-DeepfakeDetection/blob/bed4c2c9eb9b2000e9a24233d3b010afa3452f12/train.py#L69) to False. And set [test config](https://github.com/chuangchuangtan/NPR-DeepfakeDetection/blob/bed4c2c9eb9b2000e9a24233d3b010afa3452f12/train.py#L30)
```
vals = ['ADM', 'biggan', 'glide', 'midjourney', 'sdv5', 'vqdm', 'wukong']
multiclass = [ 0, 0, 0, 0, 0, 0, 0 ]
```
</details>
```
./pytorch18/bin/python train.py --dataroot {GenImage Path} --name sdv4_bs32_ --batch_size 32 --lr 0.0002 --niter 1 --cropSize 224 --classes sdv4
```
Train with sdv4 as the training set, using a random seed of 70. [Pretrained checkpoint](https://drive.google.com/drive/folders/1_mD17F94xMbJqEAsWRW1gVsZ5db6YamI?usp=sharing).
|Generator | Acc. | A.P. |
|:----------:|:----:|:----:|
| ADM | 87.8 | 96.0 |
| biggan | 80.7 | 89.8 |
| glide | 93.2 | 99.1 |
| midjourney | 91.7 | 97.9 |
| sdv5 | 94.4 | 99.9 |
| vqdm | 88.7 | 96.1 |
| wukong | 94.0 | 99.7 |
| Mean | 90.1 | 96.9 |
<!--
| <font size=2>Method</font>|<font size=2>ProGAN</font> | |<font size=2>StyleGAN</font>| |<font size=2>StyleGAN2</font>| |<font size=2>BigGAN</font>| |<font size=2>CycleGAN</font> | |<font size=2>StarGAN</font>| |<font size=2>GauGAN</font> | |<font size=2>Deepfake</font>| | <font size=2>Mean</font> | |
|:----------------------:|:-----:|:-----:|:------:|:---:|:-------:|:--:|:----:|:-----:|:-------:|:----:|:----: |:-----:|:---: |:-----:|:----:|:----:|:----:|:----:|
| | Acc. | A.P. | Acc. | A.P.| Acc. | A.P. | Acc.| A.P. | Acc. | A.P. | Acc. | A.P. | Acc. | A.P. | Acc. | A.P. | Acc. | A.P. |
| CNNDetection | 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 |
| Frank | 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 |
| Durall | 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 |
| Patchfor | 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 | 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 |
| SelfBland | 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 |
| GANDetection | 82.7 | 95.1 | 74.4 | 92.9| 69.9 | 87.9 | 76.3| 89.9 | 85.2 | 95.5 | 68.8 | 99.7 | 61.4 | 75.8 | 60.0 | 83.9 | 72.3 | 90.1 |
| BiHPF | 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 | 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 | 99.9 | 100.0 | 94.8 | 99.9| 96.0 | 99.9 | 82.9| 90.7 | 85.3 | 94.0 | 99.6 | 100.0 | 72.4 | 79.3 | 58.0 | 67.9 | 86.1 | 91.5 |
| Ojha | 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 |
| NPR(our) | 99.8 | 100.0 | 96.3 | 99.8| 97.3 | 100.0| 87.5| 94.5 | 95.0 | 99.5 | 99.7 | 100.0 | 86.6 | 88.8 | 77.4 | 86.2 | 92.5 | 96.1 |
-->
## Acknowledgments
This repository borrows partially from the [CNNDetection](https://github.com/peterwang512/CNNDetection).
|